690 Machine Learning (ML) Jobs in India

Location - Bangalore / Pune 

Exp - 4 to 10 years 

Required

·      3–8 years of relevant experience in data science, AI engineering, or applied machine learning roles.

·      Bachelor’s or master’s degree in computer science, Mathematics, Engineering, or a related quantitative field.

·      System design: ability to design scalable, reliable, and maintainable systems, including service decomposition, APIs, caching, queues and event-driven architectures, and the trade-offs between latency, throughput, cost, and consistency.

·      Strong foundation in data structures and algorithms, with the ability to analyse time and space complexity and choose efficient approaches for large-scale data processing.

·      Experience building and operating data-intensive or data-driven applications, such as large-scale data pipelines, batch and streaming processing, or applications whose core features depend on high-volume data.

·      Strong hands-on experience in Python.

·      Solid foundation in machine learning, NLP, and statistical modeling, including data exploration and visualization.

·      Hands-on experience or projects involving LLMs, LangChain, LangGraph, RAG, or agentic AI systems.

·      Experience delivering AI/ML solutions into production environments.

·      Understanding of text processing and unstructured data.

·      Knowledge of software engineering best practices (TDD, refactoring, modular design, version control).

·      System design: ability to design scalable, reliable, and maintainable systems, including service decomposition, APIs, caching, queues and event-driven architectures, and the trade-offs between latency, throughput, cost, and consistency.

·      Strong foundation in data structures and algorithms, with the ability to analyze time and space complexity and choose efficient approaches for large-scale data processing.

·      Experience building and operating data-intensive or data-driven applications, such as large-scale data pipelines, batch and streaming processing, or applications whose core features depend on high-volume data.

·      Strong analytical, problem-solving, and communication skills.

·      Self-motivated, proactive, and delivery-oriented mindset.

Role Overview

We are looking for a strong Senior Python Engineer with hands-on AI/ML and Automation experience to join

our engineering team.

The engineer will work on high-scale backend systems, intelligent automation platforms, OCR/document

processing, AI-powered workflows, browser automation and integrations across multiple products.

This is not a role for someone whose AI experience is limited to calling LLM APIs. We need an engineer who

understands Python deeply and has built, deployed and maintained real production systems.

Experience Requirements

l Minimum 4 years of professional software development experience, primarily using Python.

l Minimum 2 years of practical hands-on experience in AI/ML, intelligent automation or closely related production

systems.

l Candidates with 4-6+ years of strong relevant experience are preferred.

l Strong production/backend engineering experience is essential.

Core Python & Backend Skills

l Python; FastAPI / Django / Flask; REST APIs and backend service development.

l Async programming and concurrency; multithreading / multiprocessing where appropriate.

l PostgreSQL / MySQL; Redis; background jobs and task queues such as Celery.

l WebSockets / real-time communication where required; authentication and authorization; API integrations.

l Docker; Linux; Git; logging, debugging and production troubleshooting.

l Ability to build fast, reliable and scalable Python services rather than only scripts.

AI / ML Requirements

l Machine Learning fundamentals; model training, evaluation and inference; data preprocessing and feature

engineering.

l Scikit-learn; PyTorch or TensorFlow; NLP.

l Embeddings, semantic search and vector databases.

l LLM integrations, prompt engineering, structured output and tool/function calling.

l RAG pipelines; AI agents / agentic workflows.

l OpenAI, Gemini, Claude or equivalent integrations; local/open-source model integration where appropriate.

l Understanding of model selection, accuracy, latency and cost trade-offs.

Automation Engineering

l Playwright; Puppeteer or equivalent browser automation frameworks; Selenium where appropriate.

l Headless browser and web workflow automation; form/repetitive task automation.

l Session/cookie management; file upload/download automation; API + browser hybrid automation.

l Retry and recovery mechanisms; scheduling and background execution.

l Queue-based automation workers; parallel/concurrent task processing.

JOB DESCRIPTION

Lead Engineer

Ahmedabad, India | Around 5 years of experience

Salary is open for the right candidate.

About the role

A growing healthcare technology startup is building AI-native hospital management systems and

healthcare products for emerging markets. You’ll set technical direction, lead the engineering team, and

stay hands-on with the code that ships.

What you’ll do

Set technical direction. Make architecture decisions, weigh trade-offs, and build systems that stay

reliable as requirements evolve.

Ship production software. Write, review, test, and debug code, and own the quality of what ships.

Lead the team. Break complex problems into deliverable work, mentor engineers, and take responsibility

for delivery.

Use AI coding agents. Give agents the context to tackle substantial engineering work, then critically

review, test, and debug their output.

Work with healthcare teams. Learn their workflows and turn complex requirements into dependable

software.

What you’ll bring

Production ownership. Around five years of experience, including meaningful ownership of complex

systems or products in production. Experience can be flexible for exceptional candidates.

Technical depth. Strong ability in mathematics, algorithms, or machine learning, demonstrated through

competitions, research, or demanding systems you’ve built and deployed.

Engineering leadership. Examples of architecture decisions you owned, engineers you mentored, and

work your team delivered.

AI-assisted development skills. A workflow you can demonstrate, including how you spot incorrect

assumptions, test changes, and debug failures.

Initiative and judgment. You learn quickly, tackle unfamiliar problems independently, and get working

software into users’ hands.

How to apply

Send your CV or LinkedIn profile to the recruiter who shared this brief. Include one or two examples of

your strongest technical work, a team leadership example, and an example of your AI coding workflow.

Highlight your own contribution and share only material you’re permitted to disclose.

Please include your salary expectations, notice period, and availability to work in Ahmedabad.

We are seeking a senior Generative AI Systems Expert with 8+ years of deep engineering experience to architect production-grade AI systems (RAG pipelines, fine-tuning, prompt optimization, inference scaling). This is a specialized Individual Contributor (IC) role for a production-focused builder who owns the AI implementation end-to-end—from initial architecture to deployment, optimization, and evaluation.

While your primary mastery lies in building and scaling robust LLM applications, you possess a strong, rigorous engineering mindset. Experience or strong familiarity with Quality Assurance (QA), Test Automation, and AI evaluation frameworks is highly preferred to ensure our systems behave reliably and deterministically at scale. You are not just implementing off-the-shelf models; you are designing complex retrieval systems, optimizing inference pipelines, evaluating model trade-offs, and building systems that reliably serve millions of transactions.

🎯 What We’re Looking For (Who You Are)

• AI Systems Architect (Core): 4+ years of dedicated experience building and shipping LLM systems in production. You understand model selection, RAG architecture, fine-tuning strategies, inference optimization, and cost management. You make pragmatic trade-offs between accuracy, latency, and cost.
• Production-Focused Builder: You've shipped LLM systems at scale. You are deeply comfortable with tokenization, context windows, vector database optimization, handling hallucinations, and system failures.
• Independent Operator: You own AI features end-to-end—from problem definition through evaluation, deployment, and production monitoring. You unblock yourself and make good decisions with incomplete information.
• Quality & Evaluation-Minded (Highly Valued Plus): You possess a rigorous engineering background with a strong appreciation for testing. You understand how to handle non-deterministic outputs through deterministic test design, evaluate model performance (F1, precision, recall), and build/use automation frameworks to prevent regressions.
• Team Amplifier: You mentor engineers on AI best practices, participate in code reviews, and help scale GenAI adoption across the organization.
• Continuous Experimenter: You A/B test approaches, iterate based on production data, and stay current with the rapid evolution of LLMs.

🛠️ Technical Stack & Qualifications

GenAI Core & Architecture (Must-Have)

• LLM Ecosystem: Deep experience with LLM APIs (OpenAI, Anthropic, Hugging Face), model trade-offs, and selection.
• Retrieval: RAG pipeline architecture and vector databases (Pinecone, Weaviate, FAISS).
• Optimization: Prompt engineering, fine-tuning (PEFT/LoRA), and inference optimization.
• Operations: Cost/latency management, hallucination detection, and mitigation.
• Advanced Patterns: Agentic patterns, multi-step reasoning, and building for reliability under uncertainty.
• Education: Degree in Computer Science, Mathematics, or equivalent professional experience. A practical portfolio demonstrating GenAI/ML implementation is valued highly.

Testing & Automation (Highly Desirable / Good-to-Have)

• Domain Expertise: 5+ years in the QA/Testing domain.
• AI-Specific Validation: Regression testing for models, hallucination testing, bias detection, and mocking LLM APIs.
• Deterministic Design: Handling non-deterministic AI outputs through deterministic test frameworks.
• Automation Suite: Proficiency in test case design (positive, negative, edge cases) and frameworks like pytest, Jest, Selenium, or Cypress.
• CI/CD: Experience integrating automated test execution into CI/CD pipelines.

💼 Key Responsibilities

• Build: Design and implement end-to-end GenAI systems—RAG pipelines, fine-tuning strategies, and prompt optimization. Make pragmatic choices between model options, latency/cost trade-offs, and production constraints.
• Ship & Guard: Own the deployment of AI features. Architect the system to ensure reliability, scalability, and proper handling of edge cases (hallucinations, rate limits, failures) using robust validation strategies.
• Measure & Evaluate: Instrument systems with telemetry. Run A/B experiments comparing AI approaches. Establish metrics dashboards to track both system performance and model accuracy/alignment over time.
• Collaborate: Mentor engineers on AI best practices. Participate in code reviews. Help the team adopt and scale GenAI capabilities safely and effectively.

Educational Qualification:• B.Tech / M. Tech / M.S. in Computer Science, Data Engineering, AI or related discipline.• Certification in cloud DevOps or MLOps platforms (AWS DevOps Engineer, AzureDevOps Expert, GCP Professional ML Engineer) is highly desirable.• Contributions to MI-Ops or DevOps open source projects is preferred

Experience:• 7—10 years in machine learning operations or DevOps engineering.• Minimum 4 years building CVCD pipelines for AI/ML model deployment in enterprise orgovernment ecosystems.• Proven experience with containerized and microservice architectures.

Key Responsibilities:1.Design and manage continuous integration and delivery (CVCD) pipelines for AIIMLmodels across multiple environments.2. Establish model versioning, deployment, monitoring, and rollback mechanisms to ensurestability and traceability.3.Automate training, testing, and serving workflows using containerized solutions.4.Define infrastructure-as-code templates for scalable AI deployment on on-prem or cloud environments.5. Collaborate with Data Science and Engineering teams to standardize model input/outputformats and performance metrics.6.Implement logging, monitoring, and alerting for deployed models to ensure highavailability and accuracy over time.7.Ensure compliance with Responsible Al guidelines for deployment, including bias auditingand explainability tracking.

• Technical Competencies:• MLOps Platforms: MLflow, Kubeflow, Azure ML, AWS SageMaker Pipelines, GCP VertexAl Pipelines for end-to—end ML workflow orchestration• Containerization: Docker, Kubernetes, Helm charts, container registries, and microservicesarchitecture for ML workloads• CI/CD: Jenkins, GitLab CI, GitHub Actions, Azure DevOps with specialized ML pipelineintegration and automated testing• Infrastructure-as-Code: Terraform, CloudFormation, Ansible for reproducible MLinfrastructure provisioning and management• Cloud Platforms: AWS (EKS, Lambda, ECR, S3), Azure (AKS, Container Registry, BlobStorage), GCP (GKE, Cloud Build, Cloud Storage)• Model Serving: TorchServe, TensorFlow Serving, Seldon, KServe, REST APIs, and real-time inference infrastructure.• Programming Languages: Python for automation, Bash scripting, YAML for configurationmanagement, basic understanding Of Go/Java• Database & Storage: Feature stores (Feast, Tecton), model registries, data versioning(DVC), and distributed storage systems• Workflow Orchestration: Apache Airflow, Prefect, Argo Workflows for complex MLpipeline scheduling and dependency management.

Job Details

Position Title: AI Evangelist

Role Purpose

We are seeking a highly experienced AI Evangelist & Core AI Engineer who combines deep technical expertise in Artificial Intelligence with the ability to influence, educate, demonstrate, and accelerate AI adoption across the enterprise.

The role requires deep understanding of AI, Machine Learning, Deep Learning, Generative AI, Large Language Models, Agentic AI, AI engineering, and modern software development practices, combined with significant hands-on experience implementing scalable AI applications in complex enterprise environments.

Key Responsibilities

1. Core AI Engineering & Architecture

• Design, develop, and implement enterprise-grade AI and Generative AI applications.
• Build scalable applications using LLMs, multimodal models, AI agents, RAG, vector databases, knowledge graphs, APIs, and enterprise data platforms.
• Architect AI solutions capable of supporting enterprise-scale workloads with appropriate considerations for:
• Scalability
• Reliability
• Security
• Performance
• Observability
• Cost optimization
• Governance
• Develop and implement advanced Retrieval-Augmented Generation (RAG) architectures including hybrid search, reranking, metadata filtering, semantic retrieval, and knowledge grounding.
• Build multi-agent and agentic AI applications capable of reasoning, tool usage, workflow execution, orchestration, and enterprise system integration.
• Design AI applications integrating structured and unstructured enterprise information.
• Evaluate architecture choices between commercial models, open-source models, small language models, domain-specific models, and hosted AI platforms.
• Implement model routing, prompt orchestration, caching, guardrails, evaluation pipelines, and fallback strategies.
• Work closely with cloud, platform, security, enterprise architecture, DevOps, and application engineering teams.

2. Deep AI / ML / DL Expertise

The candidate should have strong conceptual and practical understanding across the modern AI technology landscape, including:

• Artificial Intelligence /Machine Learning/ Deep Learning/ Neural Networks /LLM/SLM/RAG/Graph RAG / MLOps / LLMOps / GenAIOps

3. Generative AI & LLM Engineering

• Build production-grade applications utilizing leading commercial and open-source LLM ecosystems.
• Design scalable LLM platforms supporting multiple enterprise applications and use cases.
• Develop reusable LLM services, APIs, components, agent frameworks, and reference architectures.

4. AI Agents & Automation

• Design and implement AI agents and agentic workflows that automate complex business and engineering processes.
• Build agents capable of interacting with enterprise applications, APIs, databases, documents, development environments, and workflow platforms.
• Evaluate emerging agent architectures and orchestration frameworks pragmatically.
• Develop reusable patterns for:
• Single-agent systems
• Multi-agent systems
• Human-in-the-loop workflows
• Autonomous workflows
• Tool-calling agents
• Coding agents
• Enterprise knowledge agents
• Establish appropriate controls for AI agent identity, permissions, auditability, security, and human oversight.

5. AI-Enabled Software Engineering

Drive adoption of AI throughout the Software Development Life Cycle.

Job Title: Engineering Manager

Location- Delhi

Job type: Full time, On site

About Us: TIMBLE is leading Authentication Company, delivering cutting edge technology and alternate data analysis for Identity management, Onboarding & Verification and Business Intelligence. We provide solutions across three verticals

1. BFSI Solutions

2. KYC and background check Solutions

3.AI Solutions

Role Overview

As an Engineering Manager at Timble AI, you will manage, mentor, and scale our core engineering teams (including Backend, Frontend, and AI/ML divisions). You will remain highly technical, contributing to architecture decisions and system design, while taking full ownership of project delivery timelines, agile sprints, and team performance metrics.

Key Responsibilities

1. People Leadership & Mentorship: Manage and grow a cross-functional team of software engineers (SDE-1 to SDE-3). Conduct regular 1-on-1s, drive career growth paths, and foster an innovative, high-ownership engineering culture.
2. Delivery & Agile Project Management: Own the engineering sprint cycles. Work closely with Product Managers to break down roadmaps into execution goals, manage dependencies, and ensure high-velocity, predictable delivery.
3. System Design & Architecture: Provide technical governance and direction. Collaborate on core system architecture, design reviews, and ensure codebases remain clean, scalable, and modular.
4.  Engineering Excellence: Define and monitor key performance indicators (KPIs) for the engineering team, including code quality, test coverage, uptime, and system performance.
5. Hiring & Scaling: Actively partner with the talent acquisition team to identify, interview, and onboard top-tier engineering talent to help build our core verticals.

Required Skills & Qualifications

1. Experience: 5+ years of core software engineering experience, with at least 1–2 years of experience in an engineering management, tech lead, or team leadership role.
2. Technical Roots: Strong foundational background in backend systems, distributed architectures, or AI integration. Prior hands-on experience with Python/Django ecosystems, microservices, cloud infra (AWS/GCP), or heavy automation systems is highly valued.
3. Agile Mastery: Deep familiarity with Agile/Scrum development methodologies, sprint planning tools, and CI/CD pipelines.
4.  Communication: Exceptional interpersonal and cross-functional communication skills, with a track record of translating complex technical visions into clear execution steps.
5.  Education: B.Tech / M.Tech in Computer Science, Information Technology, or a related technical discipline from a reputed institute.

Learn more about us at: https://timbleglance.com

About the role

You'll work alongside a Senior Backend, Data & ML Developer and our existing backend team to build backend systems, process data, and integrate ML and data science models into our platform.

What you'll do

• Build and maintain backend services and APIs.
• Develop data processing workflows and pipelines.
• Implement, integrate and maintain ML models and data science algorithms in production.
• Optimise database queries, data models and system performance.
• Work with the frontend and mobile teams on API integrations.
• Write tests and documentation, and take part in code reviews.

What we're looking for

• 3–5 years of backend development experience.
• Strong skills in Django and working knowledge of SQL databases.
• Practical experience with ML models and data science algorithms, from training or integration through to deployment.
• Experience handling and transforming data at scale.
• Experience with cloud platforms, Git and CI/CD.
• Clear communication with backend, web and mobile teammates.
• Experience working with AI tools for development such as Claude and Cursor

Note: Full-stack experience isn't required. Strong backend and data skills plus good cross-team communication matter most.

Nice to have

• Python data and ML tools (for example pandas, scikit-learn or PyTorch).
• Experience with model serving or MLOps tools.

Data Science Engineer – Python, SQL, Statistics & Machine Learning

Company: Talview

Experience: 3–5 years

Location: Bengaluru, Karnataka

Work Mode: Work from Office, with some flexibility

Work Week: 5 days

About Talview

Talview builds AI-powered hiring and assessment solutions for enterprise organisations. Our products support candidate screening, interview scheduling and online exam proctoring, helping teams improve efficiency and make better-informed decisions.

Role Overview

We are looking for a Data Science Engineer with strong hands-on experience in Python, SQL, statistics and classical machine learning, along with experience working on GenAI and Large Language Models (LLMs).

You will work across data processing, statistical analysis, experimentation, model development, evaluation and production AI/ML workflows. You will collaborate with Engineering and Product teams to translate business problems into measurable data science solutions.

This role requires practical experience in classical ML and statistics, beyond working only with LLM APIs or prompt engineering.

Roles and Responsibilities

• Develop complex SQL queries for data extraction, transformation, analysis and validation.
• Use Python for data processing, exploratory data analysis, automation and reusable experimentation utilities.
• Apply statistical analysis, hypothesis testing and experimental design to evaluate business and product outcomes.
• Develop and evaluate machine learning models using appropriate metrics and clearly defined success criteria.
• Build LLM workflows involving prompting, structured outputs and comparative evaluation of models and configurations.
• Create evaluation datasets, benchmarks, sampling strategies and ground-truth validation approaches.
• Perform error analysis and root-cause analysis to identify model failures, anomalies and performance degradation.
• Analyse production data and troubleshoot data, model and pipeline issues.
• Integrate data from APIs, databases and other structured and unstructured sources.
• Collaborate with Engineering and Product teams on ML pipelines, evaluation frameworks and inference workflows.
• Research and test techniques to improve AI/ML system performance.

Required Skills and Experience

• 3–5 years of relevant experience in Data Science, Machine Learning, AI or a related field.
• Strong hands-on Python programming skills.
• Strong SQL skills for complex querying, data transformation and analysis.
• Solid understanding of statistics, hypothesis testing, experiment design and performance measurement.
• Practical experience developing and evaluating classical machine learning models.
• Experience with LLMs, prompting, structured outputs and evaluation.
• Experience defining evaluation metrics, benchmarks and error-analysis approaches.
• Ability to work with APIs, databases and structured and unstructured datasets.
• Strong analytical and problem-solving skills.
• Ability to explain technical decisions, interpret results and communicate findings to stakeholders.

Preferred Skills

• Experience designing end-to-end ML or LLM workflows.
• Experience with dataset creation, sampling, annotation and ground-truth validation.
• Experience analysing production AI/ML systems and debugging model or pipeline issues.
• Experience building reusable experimentation and evaluation frameworks.

Key Skills

Python, SQL, Data Science, Statistics, Machine Learning, Classical ML, Hypothesis Testing, Experimental Design, Model Evaluation, Exploratory Data Analysis, GenAI, Large Language Models, LLM Evaluation, Error Analysis

Chief Engineer - Artificial Intelligence

AI Architecture | Production Engineering | LLM, RAG & Agentic AI

Primary objective: Build secure, scalable and reusable AI capabilities that move from experimentation to reliable production use across DocTutorials products and business functions.

Department

Technology & Product Engineering

Location

Hyderabad / Chennai

Reports to

Vice President - Technology

Experience

10+ years in software engineering, including 5+ years in AI/ML

Role Summary

The Chief Engineer - AI will define and lead AI engineering architecture, standards and production implementation. This is a senior, hands-on technical leadership role responsible for building reliable AI platforms and guiding AI engineers, data teams and application teams.

Key Responsibilities

1. AI Strategy and Architecture

·       Define the AI engineering strategy, architecture and technical roadmap.

·       Evaluate and select suitable models, frameworks, vector databases and AI platforms.

·       Make practical build-versus-buy and open-source-versus-commercial decisions.

·       Ensure AI solutions are modular, reusable, scalable, secure and cost-effective.

2. Production AI Engineering

·       Lead AI initiatives from use-case assessment and proof of concept through production deployment.

·       Build reusable AI services, APIs, pipelines and platform components for web, mobile and internal applications.

·       Establish development, testing, deployment, monitoring and production-support practices.

·       Resolve complex issues involving model quality, retrieval, performance, reliability and cost.

3. LLM, RAG and Agentic AI

·       Design LLM applications, enterprise RAG solutions and controlled AI-agent workflows.

·       Build document processing, chunking, embedding, indexing, retrieval and response pipelines.

·       Implement source traceability, human approval and clear permissions for sensitive use cases.

·       Improve response quality through prompt design, retrieval optimisation and evaluation.

4. AI Quality, Security and Responsible AI

·       Define evaluation standards for accuracy, relevance, groundedness, safety, latency and cost.

·       Introduce benchmark datasets and regression testing for prompts, models and retrieval pipelines.

·       Implement safeguards for hallucinations, unsupported responses, data leakage and unsafe actions.

·       Ensure role-based access, encryption, audit logging, secure secrets handling and approved data usage.

5. AI Platform and Integration

·       Create common AI capabilities and integration standards that can be reused across products and teams.

·       Integrate AI services with applications, databases and approved enterprise data sources.

·       Implement observability for model usage, quality, errors, response time and cost.

·       Reduce dependency on a single model provider, individual engineer or external vendor.

6. Technical Leadership and Team Development

·       Define AI engineering standards for architecture, coding, review, testing and documentation.

·       Remain hands-on through design reviews, critical code reviews, debugging and focused prototypes.

·       Mentor AI/ML engineers, data engineers and application teams; support hiring and capability building.

·       Work with Product and business teams to convert high-value problems into measurable AI solutions.

Required Skills and Experience

·       10+ years of software engineering experience, including 5+ years in practical AI/ML engineering.

·       Proven experience moving AI solutions from prototype to reliable production use.

·       Strong Python and production API development experience, preferably FastAPI.

·       Strong knowledge of LLMs, RAG, prompt engineering, embeddings and agentic AI.

·       Experience with vector databases such as pgvector, Qdrant, Milvus or Weaviate.

·       Experience with LangChain, LlamaIndex, Haystack or similar orchestration frameworks.

·       Knowledge of cloud architecture (preferably AWS), microservices, databases, queues and distributed systems.

·       Experience with Docker, Kubernetes, CI/CD, observability and production support.

·       Strong understanding of application security, data privacy and responsible-AI engineering.

·       Demonstrated technical leadership, mentoring and stakeholder-management capability.

Good-to-Have Experience

·       Healthcare, medical education, EdTech or digital-learning platforms.

·       Medical-content intelligence, knowledge graphs, ontologies, taxonomy or semantic search.

·       Clinical Case Library, personalised learning or recommendation systems.

·       Multi-modal AI involving text, audio, images or video.

·       Healthcare-data privacy, de-identification or standards such as SNOMED CT, ICD, HL7 or FHIR.

How Success Will Be Measured

·       Production adoption and measurable business or learner impact of AI capabilities.

·       Accuracy, groundedness, reliability, security and cost efficiency of AI solutions.

·       Reusable AI services delivered across products with reduced technical and vendor dependency.

·       Improved capability, engineering quality and ownership within the AI team.

We are looking for a hands-on AI engineering leader who can distinguish a valuable production capability from an attractive demo and build AI that is secure, scalable, measurable and maintainable.

We're hiring an AI/ML Mentor Intern for a well-established AI and data science training institute in KPHB, Hyderabad. Immediate joining.

What you'll do

• Mentor students in Python, machine learning, deep learning, NLP and computer vision

• Run doubt-clearing sessions, lab practicals and code reviews

• Help students build and debug real projects

• Prepare assignments, notebooks and assessments

Skills we're looking for

• Strong Python (NumPy, Pandas, scikit-learn)

• ML: regression, classification, clustering, model evaluation

• Deep learning with TensorFlow or PyTorch (CNNs, RNNs, Transformers)

• NLP and computer vision basics (OpenCV, Hugging Face)

• Statistics and probability

• Clear communication and patience in explaining concepts

Compensation: Up to ₹25,000 per month, based on performance. Internship leading to a full-time role.

Selection: online screening, technical interview, HR discussion.

Senior Trainer — AI/ML

Reports To: Associate Director / Head of L&D

Department: L&D-Corporate

Location: Gurgaon, Haryana & Bangalore

Job Summary

We are looking for a skilled and detail-oriented Trainer to design and deliver high-impact learning programs in AI/ML (including Generative AI), Digital Transformation, Technology, and Advanced Analytics.

The role emphasizes structured learning paths, engaging facilitation, and measurable outcomes aligned with company's domain-specific AI and analytics offerings. Key Responsibilities We seek an experienced AI Enablement professional to design and deliver high-impact learning programs in Generative AI, Agentic AI, AI Productivity, Data & Analytics, and Digital Transformation.

The role focuses on driving enterprise AI adoption through structured learning journeys, hands-on workshops, AI certifications, and business-aligned capability-building initiatives. The trainer will work closely with business leaders and SMEs to enable employees to leverage AI, automation, and intelligent agents.

Core Competencies

• Design and deliver instructor-led, virtual, and blended learning programs on Generative AI, Agentic AI, Large Language Models (LLMs), AI Engineering, Data Science, and enterprise AI platforms such as Microsoft Copilot, Copilot Studio, and Azure AI Foundry.

• Develop role-based curricula, learning journeys, certification pathways, and capability frameworks aligned to emerging AI technologies and business needs.

• Deliver hands-on workshops on AI applications, prompt engineering, AI agents, enterprise copilots, and workflow automation.

• Create practical learning experiences using tools such as Microsoft 365 Copilot, Copilot Studio, ChatGPT, Claude, Azure AI Services, and other enterprise AI platforms.

• Track learning effectiveness, adoption, business impact, and ROI using learner feedback, usage analytics, and performance metrics.

• Develop reusable prompts, agent templates, AI accelerators, and knowledge assets to support scalable learning initiatives.

• Leverage digital learning technologies, AI-powered learning platforms, and modern instructional methodologies to enhance learner engagement.

• Stay abreast of advancements in GenAI, Agentic AI, AI governance, Responsible AI, automation, and emerging technologies and continuously integrate them into learning programs.

Required Qualifications

• Master’s degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Analytics, or a related discipline

• 6–10 years of corporate training/L&D experience with strong focus on technology and analytics would be an added advantage.

• Hands-on expertise in Python and practical AI solution development, with experience in Generative AI tools, prompt engineering, and AI-powered productivity platforms.

• Working knowledge of LLM ecosystems, Retrieval-Augmented Generation (RAG), AI agents, automation frameworks, and enterprise AI applications.

• Experience with Microsoft Copilot, Copilot Studio, Power Platform, Azure AI Services, OpenAI technologies, or equivalent AI ecosystems.

• Strong understanding of cloud platforms such as Azure, AWS, or Google Cloud, with familiarity in AI and machine learning services.

Preferred Qualifications

• AI/ML & Generative AI concepts; model lifecycle and governance awareness.

• Instructional design, curriculum development, and assessment design.

• Strong facilitation, storytelling, and stakeholder management.

• Data-driven evaluation; dashboards and reporting for learning impact.

• Familiarity with company's platforms and decision-centric analytics approach.  

Thanks & Regards,

Arti Chopra

Bean HR Consulting-"Ahead Your Search"

https://beanhr.com

Locations:- Ghaziabad (Delhi-NCR) / Dehradun

Key Responsibilities

We seek an experienced AI Enablement professional to design and deliver high-impact learning programs in Generative AI, Agentic AI, AI Productivity, Data & Analytics, and Digital Transformation. The role focuses on driving enterprise AI adoption through structured learning journeys, hands-on workshops, AI certifications, and business-aligned capability-building initiatives. The trainer will work closely with business leaders and SMEs to enable employees to leverage AI, automation, and intelligent agents.

Core Competencies

• Design and deliver instructor-led, virtual, and blended learning programs on Generative AI, Agentic AI, Large Language Models (LLMs), AI Engineering, Data Science, and enterprise AI platforms such as Microsoft Copilot, Copilot Studio, and Azure AI Foundry.
• Develop role-based curricula, learning journeys, certification pathways, and capability frameworks aligned to emerging AI technologies and business needs.
• Deliver hands-on workshops on AI applications, prompt engineering, AI agents, enterprise copilots, and workflow automation.
• Create practical learning experiences using tools such as Microsoft 365 Copilot, Copilot Studio, ChatGPT, Claude, Azure AI Services, and other enterprise AI platforms.
• Track learning effectiveness, adoption, business impact, and ROI using learner feedback, usage analytics, and performance metrics.
• Develop reusable prompts, agent templates, AI accelerators, and knowledge assets to support scalable learning initiatives. Sensitivity: Public evalueserve.com
• Leverage digital learning technologies, AI-powered learning platforms, and modern instructional methodologies to enhance learner engagement.
• Stay abreast of advancements in GenAI, Agentic AI, AI governance, Responsible AI, automation, and emerging technologies and continuously integrate them into learning programs.

Required Qualifications

• Master’s degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Analytics, or a related discipline
• 6–10 years of corporate training/L&D experience with strong focus on technology and analytics would be an added advantage.
• Hands-on expertise in Python and practical AI solution development, with experience in Generative AI tools, prompt engineering, and AI-powered productivity platforms.
• Working knowledge of LLM ecosystems, Retrieval-Augmented Generation (RAG), AI agents, automation frameworks, and enterprise AI applications.
• Experience with Microsoft Copilot, Copilot Studio, Power Platform, Azure AI Services, OpenAI technologies, or equivalent AI ecosystems.
• Strong understanding of cloud platforms such as Azure, AWS, or Google Cloud, with familiarity in AI and machine learning services. Preferred Qualifications
• AI/ML & Generative AI concepts; model lifecycle and governance awareness.
• Instructional design, curriculum development, and assessment design.
• Strong facilitation, storytelling, and stakeholder management.
• Data-driven evaluation; dashboards and reporting for learning impact.

Role Summary 

We are seeking an Advanced Machine Learning Engineer to design, build, train, deploy, and operate scalable machine learning systems. This hands-on ML engineering role focuses on implementing models and production systems—not solely conducting data analysis or research. It requires strong Python and deep-learning expertise, experience with frameworks such as PyTorch or TensorFlow, and the ability to own the end-to-end model lifecycle, from behavioral/event data preparation and feature engineering through model development, large-scale training, deployment, inference optimization, and monitoring. The engineer will work with Snowflake, Databricks, Spark, and GPU-based training infrastructure to deliver reliable, efficient, and maintainable ML capabilities. 

Must have - Buulding and operated production ML systems using Python + PyTorch /TensorFlow + Snowflake/Databricks + MLOps. 

Key Responsibilities 

Machine Learning Engineering 

• Implement and optimize machine learning algorithms and models for business use cases, including classification, regression, clustering, and recommendation systems as applicable. 
• Translate model requirements into robust, reusable, and testable Python components. 
• Build model training, validation, scoring, and inference workflows that can be integrated into production applications. 
• Evaluate model performance against agreed technical and business metrics and implement improvements to accuracy, latency, robustness, and resource efficiency. 
• Apply appropriate validation, error analysis, and model testing practices to ensure reliable behavior. 

Train models efficiently on GPU infrastructure using techniques such as mixed-precision training, gradient accumulation, checkpointing, memory optimization, and distributed training strategies where appropriate. 

Use PyTorch, TensorFlow,Pyspark or equivalent frameworks to implement experiments and production-ready training code; apply rigorous evaluation, error analysis, and reproducibility practices. 

Build model-training workflows covering event/token sequence preparation, tokenization, temporal and positional representations, self-supervised pretraining, and downstream fine-tuning when appropriate to the use case. 

Design, implement, train, and optimize deep-learning models; develop custom architectures where required, including transformer- and attention-based models for sequential or behavioral data. 

Deep Learning & Model Architecture 

Data Platforms & ML Pipelines 

• Use Snowflake and SQL to access, query, and prepare enterprise data required by machine learning applications. 
• Use Databricks for scalable data processing, feature preparation, model development, and ML pipeline execution. 
• Develop and maintain reliable pipelines for data preparation, feature generation, training, batch scoring, and inference. 
• Use PySpark and distributed processing techniques where appropriate for large-scale workloads. 
• Work with data engineering teams to integrate upstream data sources and ensure dependable data contracts, quality checks, and pipeline operation. 

Productionization & MLOps 

• Productionize ML solutions and integrate them with services, APIs, data products, or downstream business workflows. 
• Implement model and code versioning, experiment tracking, reproducible builds, automated testing, and release practices. 
• Monitor model outputs and pipeline health; troubleshoot failures, data changes, drift, and performance degradation. 
• Improve operational reliability, scalability, observability, and maintainability of ML systems. 
• Collaborate with platform, cloud, and DevOps teams on deployment automation and production support. 

Build observable production ML systems with appropriate logging, alerting, performance tracking, and operational safeguards. 

Optimize inference latency, throughput, memory use, and serving cost through model architecture choices, batching, and serving-side optimizations. 

Software Engineering & Technical Ownership 

• Develop clean, modular, documented, and maintainable code following engineering standards and design principles. 
• Contribute to technical design, code reviews, testing strategies, and architectural decisions for ML systems. 
• Own assigned ML engineering deliverables from technical design through implementation, release, and ongoing support. 
• Identify performance bottlenecks and improve runtime, resource utilization, and processing efficiency. 
• Collaborate with cross-functional engineering and product teams to deliver dependable ML capabilities. 

Required Skills & Qualifications 

• Strong proficiency in Python and software engineering practices, including object-oriented design, modular development, testing, debugging, and code quality. 
• Strong ML foundation and mathematical understanding 
• Strong understanding of the end-to-end ML lifecycle, including model architecture, training, evaluation, optimization, inference, production integration, and monitoring. 
• Strong SQL skills and experience working with structured data and data pipelines. 
• Hands-on experience with Snowflake for querying and preparing data for ML workflows. 
• Hands-on experience with Databricks for data processing and/or ML pipeline development. 
• Strong hands-on experience with PyTorch, TensorFlow, or a similar deep-learning framework; experience with scikit-learn is also useful where appropriate. 
• Understanding of production ML concepts, including reproducibility, model versioning, deployment, monitoring, and troubleshooting. 
• Ability to design reliable, scalable, and maintainable software components and pipelines. 

Familiarity with GPU training concepts and distributed strategies such as Distributed Data Parallel (DDP), Fully Sharded Data Parallel (FSDP), or equivalents is an advantage for large-model workloads. 

Understanding of model optimization, inference efficiency, and integration with production services or applications. 

Experience developing and deploying ML solutions at scale, including end-to-end model lifecycle ownership. 

Preferred Skills 

• PySpark, Apache Spark, and distributed processing for large-scale behavioral or event data. 
• MLflow or similar tooling for experiment tracking, model registry, reproducible training, and model lifecycle management. 
• Experience integrating Snowflake and Databricks within enterprise ML workflows. 
• Cloud experience with AWS, Microsoft Azure, or Google Cloud. 
• REST APIs, containers, CI/CD, automated testing, logging, and observability. 
• Experience with fraud detection, anomaly detection, behavioral modeling, identity or account-abuse signals, recommendation systems, or other large-scale ML applications. 
• Exposure to Generative AI, LLM integration, embeddings, or RAG as an additional advantage. 

Education & Experience 

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Computer Engineering, or a related field, or equivalent practical experience. 
• At least 5 years of professional experience building, deploying, and supporting machine learning solutions at scale, or equivalent experience demonstrating the required engineering depth. 
• Demonstrated ability to take ownership of engineering deliverables and work through implementation, deployment, and operational issues. 

Role Overview: Take full ownership of infrastructure architecture, scaling, and uptime. Establish the technical roadmap, shape and formalize the SRE function, and coach engineers. Design, deploy, and run AI-scale infrastructure that enables real-time healthcare workflows.

Serve as a Founding Member, owning end-to-end infrastructure capabilities and introducing AI-first DevOps, SRE, and MLOps best practices, with direct collaboration alongside the Founder, Co-Founder, and Head of Engineering.

Key Responsibilities:

Architecture & Platform

• Design scalable, secure, cost-efficient AWS/GCP architectures for AI and healthcare workloads
• Lead IaC standards and reusable modules org-wide; own GitOps practices
• Architect optimized Kubernetes (EKS/GKE) for HA, scale, and GPU workload orchestration

Reliability Engineering

• Define and drive reliability targets (SLOs/SLIs); establish SRE practice
• Lead incident management, RCA, and blameless post-mortems for critical systems
• Partner on infrastructure hardening and compliance (SOC 2, HIPAA)

AI-First Engineering

• Lead AI/ML inference and GPU infrastructure scaling in production
• Evaluate and introduce AI tooling, platforms, and automation best practices
• Contribute to LLM deployment pipelines and AI systems reliability

Leadership & SDLC

• Mentor L1/L2 engineers; influence cross-team infrastructure decisions
• Drive DevSecOps maturity across the SDLC (policy-as-code, SBOM, supply-chain security)
• Evaluate and introduce new tooling and platforms

Key Requirements:

• 4–8 years infrastructure/platform/DevOps/SRE
• Deep expertise in AWS & GCP large-scale production systems
• Advanced Kubernetes (cluster design, multi-tenant ops, resource quotas)
• AI/ML inference or GPU infrastructure scaling (production track record)
• Strong Terraform / equiv. and GitOps practices
• Python, Go, or Bash scripting and automation
• Track record in scale, reliability, and cost efficiency
• Lead incident response and on-call practices

Nice-to-Have

• LLM Gateway architecture and optimization
• Zero-trust networking and advanced security/compliance (SOC 2, HIPAA)
• DevSecOps maturity: SBOM, supply-chain security, policy-as-code
• Open-source contributions or platform-team leadership

Why This Role?

• Build infrastructure and products powering real-world healthcare AI
• Work with AI-native engineering teams using cutting-edge tools (Claude Code, Cursor, Copilot)
• Mission-driven impact — improve patient outcomes and save thousands of staff hours
• High ownership, rapid learning, and significant career upside
• Global collaboration across India and the US
• Seed-stage growth — scale from Seed to Series A alongside the team
• Founding Member role and own the entire infrastructure capabilities, including bringing best AI-first practices in DevOps, SRE and MLOps; direct access to work with the Founder, Co-Founder and Head of Engineering

Position Overview:

We are building a new retail merchandising platform on top of that data. It answers what to stock, how much,

and in which store—combining sales, catalogue, and customer data with GeoIQ’s location attributes at retail

scale: thousands of stores, tens of thousands of SKUs, decisions made weekly.

As part of the data science team on this platform, you will build the forecasting, assortment and

recommendation models behind those decisions. The platform is new, so the models are being built now, not

maintained. Hands-on modelling with senior review — what you ship changes what a store actually orders.

Responsibilities:

● Build SKU- and store-level demand forecasts, including for new products with little or no sales history.

● Build assortment models — which products earn shelf space in which stores or store clusters, and how

a range change is likely to play out.

● Build recommendation and ranking models for product discovery, cross-sell and bundling, including

cold-start for new products and customers.

● Engineer features from sales, seasonality, promotions, pricing, catalogue hierarchy, store attributes and

GeoIQ’s location and demographic layers.

● Handle the realities of retail data: intermittent demand, stockouts censoring true demand, returns and

cannibalisation between similar SKUs.

● Design backtests and A/B tests that reflect how planners use the output; monitor deployed models for

drift.

● Build data pipelines and work with engineering to take models to production.

● Communicate findings to merchandising, category, and product teams.

Expectations:

● Proficient in Python

● Work experience: 1–3 years

● Prior experience with building ML models end-to-end

● Prior experience with SQL

● Knowledge of statistical techniques and model evaluation

● Experience with time-series forecasting or recommender systems will be an added advantage

● Experience with retail, e-commerce, supply chain, or spatial data will be an added advantage

Join us early and help build the merchandising brain for retail at national scale — close to the product and the

customer, with the freedom of a platform still being shaped.

Data Scientist – Machine Learning & Applied Analytics. 

Location: Bengaluru 

Experience: 5–8 Years 

Role Type: Full-Time 

Business Unit: Products & Platforms (PMG) 

Role Overview 

We are looking for a Data Scientist – Product Analytics & Machine Learning to help build differentiated analytics and ML capabilities across our product and platform portfolio, including MOBIUS+ and Signate. 

This is a hands-on product-focused role responsible for translating real-world customer and domain problems into scalable analytics and machine learning solutions. The role will work across the complete lifecycle — from data exploration and experimentation through model development, validation, productization and production monitoring. 

The Data Scientist will also work closely with Tata Elxsi's internal analytics teams, specialist solution providers and academic/research institutions to evaluate, integrate and evolve analytical capabilities as part of our products. 

Key Responsibilities 

Product Analytics & ML 

• Translate product and customer problems into well-defined analytics and ML use cases. 
• Perform data exploration, feature engineering, statistical analysis and experimentation. 
• Develop, train, validate and optimize machine learning and predictive models. 
• Define appropriate model performance metrics, benchmarks and acceptance criteria. 
• Convert prototypes and experiments into scalable, production-ready capabilities. 
• Work with Product, Architecture and Engineering teams to integrate models and analytics into SaaS products and platforms. 

Model Evaluation & Productization 

• Evaluate models and analytical solutions developed by internal teams, technology partners and specialist solution providers. 
• Assess model assumptions, data requirements, accuracy, robustness, explainability and limitations. 
• Benchmark alternative models/approaches and provide evidence-based recommendations. 
• Establish practices for model versioning, validation, monitoring, drift detection and continuous improvement. 
• Ensure that externally sourced models can be effectively integrated, governed and evolved within the product. 

Collaboration & Research 

• Collaborate with Tata Elxsi's broader AI, analytics and domain teams and leverage specialist capabilities where required. 
• Work technically with external solution providers to understand and integrate their algorithms and models. 
• Engage with researchers and academic institutions such as IITs, NITs and IISc on advanced analytics and ML opportunities. 
• Evaluate research, algorithms and PoCs and help translate promising approaches into production-grade product capabilities. 
• Build reusable analytical assets, datasets, features, evaluation frameworks and know-how across the product portfolio. 

Technical Skills 

• Strong foundation in statistics, probability, machine learning and data analysis. 
• Strong hands-on programming skills in Python and common data science libraries. 
• Experience with Pandas, NumPy, scikit-learn and relevant ML frameworks. 
• Experience developing predictive models using structured and time-series data. 
• Understanding of supervised/unsupervised learning, anomaly detection, forecasting, classification and regression techniques. 
• Experience with feature engineering, model selection, hyperparameter optimization and model evaluation. 
• Good understanding of model explainability, bias, uncertainty and robustness. 
• Working knowledge of SQL and large-scale data processing. 
• Experience deploying or integrating ML models into production applications through APIs, services or ML pipelines. 
• Understanding of MLOps, including model versioning, experiment tracking, monitoring and model lifecycle management. 
• Experience with cloud environments, preferably AWS, is desirable. 
• Exposure to GenAI/LLMs and their application within product analytics is an advantage. 

Product & Engineering Mindset 

• Ability to move beyond experimentation and think about how analytics becomes a repeatable product capability. 
• Strong problem-solving ability and comfort working with incomplete or imperfect real-world datasets. 
• Ability to distinguish between an interesting analytical result and one that creates measurable customer/product value. 
• Comfortable working closely with Product Managers, Domain SMEs, Architects and Software Engineers. 
• Ability to critically evaluate third-party algorithms and models rather than treating external solutions as black boxes. 
• Strong technical communication skills and ability to explain analytical concepts and model outcomes to both technical and business stakeholders. 

Preferred Experience 

• 5–8 years of relevant experience in Data Science, Machine Learning, Applied Analytics or related roles. 
• Experience building ML/analytics capabilities that have been deployed into production products or platforms. 
• Experience working with automotive, mobility, connected vehicle, battery/EV, IoT or industrial datasets is desirable but not mandatory. 
• Experience collaborating with technology partners, research organizations or academic institutions is an advantage. 
• Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related quantitative discipline preferred. 

What Success Looks Like 

The successful candidate will help PMG establish a strong internal Data Science & ML capability that can build differentiated analytics, intelligently leverage Tata Elxsi's broader capabilities and external specialists, and retain the knowledge and ownership required to continuously evolve analytics as a core part of our products and platforms. 

About the Role

We are looking for an AI/ML Engineer with 4+ years of Strong hands-on experience building and deploying production-grade AI systems. The ideal candidate should have strong expertise in Computer Vision, Generative AI, RAG pipelines, Voice AI, and LLM-powered applications, with experience delivering scalable, production-ready solutions across enterprise, healthcare, or SaaS environments

Key Responsibilities

• Design, develop, and deploy production-ready AI/ML and Generative AI applications, Computer Vision solutions.
• Build and optimize LLM-powered applications for real-world business use cases.
• Develop and maintain RAG (Retrieval-Augmented Generation) pipelines using vector databases and embedding models.
• Build Voice AI solutions including speech-to-text (STT), text-to-speech (TTS), conversational AI, and voice-enabled assistants.
• Create and optimize prompt engineering strategies to improve LLM output quality, consistency, and reliability.
• Work with open-source LLMs for inference, fine-tuning, evaluation, and deployment.
• Implement model monitoring, evaluation, guardrails, and performance optimization.
• Collaborate with DevOps and cloud teams for scalable deployments.
• Optimize AI systems for latency, cost, scalability, and production performance.
• Integrate AI services through APIs and enterprise applications.

Required Skills & Experience

• 4+ years of experience in AI/ML engineering and production deployments.
• Strong experience in Python and AI/ML development.
• Hands-on experience with Generative AI and LLM applications.
• Strong hands-on experience in Computer Vision using Python and frameworks such as OpenCV, PyTorch, and TensorFlow.
• Experience with Computer Vision tasks such as object detection, image classification, image segmentation, and feature extraction.
• Familiarity with models and libraries such as YOLO, Detectron2, MMDetection, and torchvision.
• Experience with image preprocessing, data augmentation, model training, evaluation, and inference optimization.
• Exposure to OCR technologies such as Tesseract or EasyOCR and vision-based deep learning models is an advantage.
• Experience building RAG pipelines using:
• Vector Databases: Pinecone, Weaviate, FAISS, ChromaDB, Milvus
• Embeddings and retrieval optimization
• Experience with Prompt Engineering techniques for LLM optimization.
• Experience with Open Source LLMs such as:
• Llama, Mistral, Falcon, Gemma, DeepSeek, Qwen
• Experience with Voice AI technologies:
• Speech-to-Text (Whisper, Deepgram, Google STT)
• Text-to-Speech (ElevenLabs, Azure Speech, Amazon Polly)
• Conversational Voice Agents
• Experience using frameworks/tools:
• LangChain, LangGraph, LlamaIndex, Hugging Face
• Understanding of fine-tuning, model evaluation, inference optimization, and AI observability.
• Experience deploying applications using:
• Docker, Kubernetes, CI/CD
• AWS / Azure / GCP

Preferred Qualifications

• Experience delivering AI solutions for Healthcare, Enterprise, or SaaS platforms.
• Exposure to MLOps practices and model lifecycle management.
• Understanding of AI governance, security, and responsible AI practices.
• Experience integrating AI with APIs and production services.

Nice to Have

• Knowledge of agentic AI workflows and orchestration.
• Experience with monitoring and evaluation frameworks.
• Exposure to multimodal AI applications

Education Qualifications

• B.Tech / M.Tech in CSE / IT / AI&ML only from good college.

ou will work closely with AI engineers, product teams, and domain experts to build intelligent document processing and knowledge automation solutions. Responsibilities include:

• Develop AI-powered document ingestion and processing pipelines.
• Build knowledge extraction engines using NLP, Machine Learning, and Generative AI.
• Develop document-to-knowledge compilers that transform PDFs, Word documents, policies, regulations, and manuals into structured knowledge.
• Build AI agents and Agentic AI workflows for enterprise automation.
• Develop rule extraction and rule generation frameworks from business documents.
• Create semantic search and enterprise knowledge retrieval solutions.
• Develop backend APIs and AI services using Python.
• Design and optimize document processing pipelines for scalability and performance.
• Build and maintain MySQL databases for knowledge repositories and AI applications.
• Integrate LLMs from providers such as OpenAI, Azure OpenAI, Gemini, Claude, and open-source ecosystems.
• Collaborate with cross-functional teams to design scalable AI architectures.
• Participate in code reviews and follow software engineering best practices.
• Continuously evaluate and adopt emerging AI and GenAI technologies.

REQUIRED SKILLS — PROGRAMMING & DEVELOPMENT

• Strong proficiency in Python and a solid understanding of Object-Oriented Programming (OOP).
• Experience building REST APIs and backend services.
• Experience working with JSON, XML, and structured data formats.
• Knowledge of software design patterns and scalable architecture.
• Strong debugging, analytical, and problem-solving skills.

ARTIFICIAL INTELLIGENCE & MACHINE LEARNING

• Strong understanding of Machine Learning concepts and algorithms.
• Experience with Natural Language Processing (NLP) and Generative AI solutions.
• Hands-on experience with Large Language Models (LLMs).
• Understanding of prompt engineering techniques.
• Knowledge of Agentic AI concepts and AI agent architectures.
• Experience developing AI-powered applications and workflows.

TECHNOLOGY STACK

AI & GenAI: Python · OpenAI · Azure OpenAI · Hugging Face · Agentic AI Frameworks · LLMs · NLP · Machine Learning

Knowledge Systems: ChromaDB · Pinecone · FAISS · Vector Databases · Semantic Search

Document Processing: Docling · OCR · PDF Processing · Apache Tika · Document Intelligence

Backend: Python · FastAPI · Flask · REST APIs

Database: MySQL

Tools & Technologies: Git · Docker · JSON · CI/CD · Azure / AWS / GCP

Designation: AI Adoption specialist/AI Consultant

Location: Ahmedabad

Work Timings: Primarily Normal Shift (Flexible to support US clients)

Role Overview:

We are seeking a highly skilled and client-centric AI Consultant/AI Adoption Specialist to join our growing team. In this pivotal role, you'll serve as a vital link between our clients' strategic objectives and the transformative power of AI. You'll primarily focus on understanding their needs, scoping opportunities, and architecting actionable AI roadmaps.

Key Responsibilities:

 Collaborate closely with clients to understand their challenges and identify opportunities to apply AI.

 Assess client requirements and prepare solution strategies using AI tools and methodologies.

 Work with internal teams to design, propose, and help execute AI-powered solutions.

 Provide AI-based recommendations that align with the client’s business objectives.

 Communicate technical possibilities in a business-friendly manner to decision-makers.

 Take ownership of the client journey from discovery to implementation and support.

 Stay updated with AI trends, tools, and real-world use cases that can benefit clients.

Required Skills & Qualifications:  Minimum 2+ Years of hands on experience into Custom AI Development.  Minimum 3+ years of experience in roles like Project Manager, Customer Success Manager, or Account Manager, preferably in a service-based company or digital agency.  Strong understanding of AI concepts, trends, and tools (e.g., NLP, ML, Chatbots, Automation, native cloud technologis).  Some hands-on experience in AI projects – either through execution, coordination, or implementation.  Ability to manage multiple client engagements and communicate effectively with both technical and non-technical stakeholders.  Strong problem-solving mind set with the ability to translate business needs into AI opportunities.  Flexible to work with international clients, especially in the US time zone as needed.

Job Overview

We are looking for an experienced AI/ML Engineer with strong full-stack development skills to design, develop, and deploy AI-powered applications. The ideal candidate should be proficient in Python backend development, frontend technologies, and LLM integration, with the ability to build end-to-end AI-native products, custom dashboards, and lightweight applications.

This role focuses on applying AI to real-world business problems through rapid prototyping, prompt engineering, Retrieval-Augmented Generation (RAG), and scalable application development.

Key Responsibilities

• Design, develop, and deploy end-to-end AI-powered applications.
• Build backend services and REST APIs using Python.
• Develop responsive frontends using React, Next.js, or similar frameworks.
• Integrate LLM APIs such as OpenAI, Claude, and Hugging Face into production applications.
• Build custom dashboards, internal tools, micro-apps, and AI-driven business solutions.
• Implement prompt engineering, embeddings, RAG pipelines, and context-aware AI systems.
• Rapidly prototype and validate ideas using modern AI-assisted development workflows.
• Optimize application performance, scalability, reliability, and AI usage costs.
• Work with data processing libraries such as Pandas and NumPy.
• Collaborate with product and business teams to translate requirements into functional AI solutions.
• Take ownership of features from ideation and development through testing, deployment, and production support.

Required Skills & Qualifications

• 2+ years of experience in software engineering, AI engineering, or application development.
• Strong proficiency in Python and backend development.
• Hands-on experience with React, Next.js, or similar frontend technologies.
• Practical experience integrating LLM APIs such as OpenAI, Claude, or Hugging Face.
• Understanding of REST APIs, microservices, and system design principles.
• Knowledge of prompt engineering, embeddings, and RAG architectures.
• Experience with data processing using Pandas and NumPy.
• Ability to independently build, test, and deploy full-stack applications.
• Strong problem-solving, debugging, and analytical skills.
• Ability to work in a fast-paced environment with a strong ownership mindset.

Good to Have

• Experience with LangChain, LlamaIndex, or similar AI frameworks.
• Familiarity with vector databases such as Pinecone, Weaviate, or Supabase.
• Experience building AI chat interfaces, copilots, assistants, and custom dashboards.
• Familiarity with cloud platforms, application deployment, and production monitoring.
• Experience with AI-assisted coding tools and rapid prototyping workflows.

What We Are Looking For

• A full-stack AI engineer who is comfortable working across both backend and frontend development.
• Someone focused on building practical AI-powered products rather than only training machine learning models.
• A developer who can prototype quickly, validate ideas, and iterate efficiently using a “vibe coding” approach.
• A self-driven professional who can take ownership of a feature from concept to production.

Please note that this job is only for Ahmedabad location.

About the Company: 

WebMD Health Corp., an Internet Brands Company, is the leading provider of health information services, serving patients, physicians, health care professionals, employers, and health plans through our public and private online portals, mobile platforms, and health-focused publications. The WebMD Health Network includes WebMD Health, Medscape, Jobson Healthcare Information, prIME Oncology, MediQuality, Frontline, QxMD, Vitals Consumer Services, MedicineNet, eMedicineHealth, RxList, OnHealth, Medscape Education, and other owned WebMD sites. WebMD®, Medscape®, CME Circle®, Medpulse®, eMedicine®, MedicineNet®, theheart.org®, and RxList® are among the trademarks of WebMD Health Corp. or its subsidiaries. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.

For Company details, visit our website: www.webmd.com

Experience: 3+ years

Qualification: Master's degree in Computer Science, Statistics, Mathematics, or a related field.

Work Timings: 2 PM to 11 PM IST

Position Requirement:

• Write clean and efficient code in Python and/or R for data analysis, statistical modeling, and machine learning tasks.
• Apply statistical and machine learning techniques to analyze large datasets and extract meaningful patterns, trends, and insights. Develop and implement predictive models for various business applications.
• Formulate hypothesis tests and design experiments to validate assumptions, measure the impact of changes, and optimize decision-making processes.
• Demonstrate strong SQL skills for efficient data querying and manipulation using window functions.
• Work with large datasets and ensure data quality and accuracy.
• Experience with dashboarding tools like Tableau is a plus to visualize and present data-driven insights to both technical and non-technical stakeholders.
• Ability to communicate complex technical concepts and insights in a clear and concise manner to both technical and non-technical audiences. Engage with the leadership team and clients to understand business requirements and provide data-driven solutions.
• Work with real world case studies in Data Science and a chance to implement various modelling techniques. Opportunity to independently execute and lead analytical projects and assignments.
• Help solve some challenging Healthcare related digital marketing problems globally. Transform business question into data requirements; collect and merge the data; analyse the data, link it to the business reality and present the results
• Develop predictive models and machine learning algorithms to study change in physician prescribing behaviour as well as WebMD integrated campaign response behaviour. Build analysis to understand user engagement and behaviours across various WebMD products.

Technical Skills needed:

1.Experience with data manipulation in SQL environment is a must. Experience with BigQuery/Vertica data warehousing is good to have

• Experience with statistical and data manipulation tools such as Python or R is a must 
• Need very good expertise in using Microsoft Excel, and Microsoft PowerPoint. Excellent presentation skills required
• Experience developing statistical models like hypothesis testing, regression models, classifications models, forecasting etc is a must 
• Algorithm designing and implementation skills in R or Python is required 

Requirements

• B.Tech/B.E. / MSc Statistics from premier institutes with
• Minimum 80% marks in 10th and 12th
• Minimum 75% marks in graduation
• 2-5 years of relevant work experience in analytics field
• Proven experience in data science, statistical modeling, and machine learning applications.
• Strong proficiency in Python and/or R programming languages.
• Solid understanding of statistical concepts, hypothesis testing, and experimental design
• Expertise in SQL for data querying and manipulation.
• Familiarity with dashboarding tools is a plus.
• Excellent presentation, communication and interpersonal skills.

Campus Ambassador — OmniRush

We’re looking for students to introduce OmniRush to their college communities and help peers build with AI.

What you’ll do

• Share OmniRush through campus groups and short demos.
• Host hands-on sessions using materials we provide.
• Help students get started and share your own projects.

Who can apply

Students aged 18+ with an interest in AI, good communication skills, and initiative. College club experience is helpful but not required.

What you’ll get

• Higher daily AI allowances as your referrals become active users.
• Bronze, Silver, and Gold ambassador tiers and leaderboard recognition.
• Opportunities for standout projects to be featured.
• Slides, demo scripts, posters, and a personal invite link.

Commitment: Around 2–3 hours weekly, plus campus sessions.

Compensation: AI allowance boosts apply while active. Reward details will be announced before the cohort begins.

Starts: 27 October 2026 · 25 positions

Apply: omnirush.ai/ambassadors

Job Summary

The Data Scientist / Analyst Consultant will design, develop and deploy advanced analytics and AI/ML models to detect fraud, waste and abuse across the AB PM-JAY ecosystem. The role involves working with large volumes of claims, beneficiary, provider and transaction data, engineering fraud features, building and validating detection triggers, and converting model outputs into investigation-ready suspicious claims and cards. The Data Scientist will build triggers using advanced analytics techniques including NLP, OCR, Social Network Analysis and deep learning, support their validation with the National Anti-Fraud Unit (NAFU), and continuously improve model efficacy based on feedback and confirmed fraud outcomes.

Key Responsibilities

• Design, develop and deploy statistical and machine learning models for fraud, waste and abuse detection on scheme claims and card data.

• Perform advanced data analytics including clustering, classification, profiling, anomaly detection, predictive modelling, scoring, regression, link prediction, co-occurrence grouping, similarity matching and causal modelling.

• Develop fraud detection triggers using advanced analytics techniques such as NLP, OCR, Social Network Analysis and deep learning, and document trigger construct, logic and thresholds for review and approval.

• Apply both supervised and unsupervised learning approaches, training models on labelled historical fraud data and scheme ecosystem data.

• Conduct data profiling, quality assessment, cleaning, standardisation and feature engineering to create analysis-ready datasets.

• Execute triggers and models on claim and card data within defined turnaround timelines and generate case-level suspicious output with trigger reasons and evidence fields.

• Validate model outputs, assess false positive risk, calibrate thresholds and measure trigger efficacy against defined performance benchmarks. • Fine-tune and retrain models on the basis of subsequent events, reviewer feedback and confirmed fraud outcomes.

• Collaborate with medical experts, business analysts, subject matter experts and technology teams to translate domain knowledge and clinical rules into executable analytical logic.

• Prepare model documentation, trigger submission packs, dashboards and reports for internal review committees and client-facing evaluations. 

Preferred Skills

• Experience in healthcare analytics, health insurance claims analytics or fraud, waste and abuse detection.

• Experience in fraud analytics roles with a proven track record of developing and implementing fraud detection models and strategies.

• Knowledge of AB-PMJAY / health benefit packages / claims adjudication workflows.

• Exposure to Social Network Analysis, graph analytics or link analysis for collusion and network detection.

• Familiarity with OCR, image analytics and document forgery or duplication detection.

• Experience with model deployment, versioning and MLOps practices.

• Exposure to dashboarding and BI tools such as Power BI, Tableau or equivalent. 

Experience

• Minimum 5 years of experience in analytics and managing big data, out of which at least 3 years in developing AI/ML models.

• Demonstrable credentials of having worked on and developed AI/ML models independently.

• Experience in fraud analytics, anomaly detection or risk modelling will be preferred.

• Experience in healthcare, health insurance or government health scheme data will be an added advantage.

Job Title: Java Lead Developer – Cucumber & AI

Experience: 12-15 Years

Required Skills: Java, Cucumber, AI

Job Description:

We are looking for a Java Lead Developer with strong experience in Java development, Cucumber-based testing, and at least 1 year of experience in AI. The candidate should have good technical and leadership skills, with the ability to guide the development team and ensure quality delivery.

Key Responsibilities:

Lead Java development activities and guide the team in designing and developing applications. Work with Cucumber for automated testing and BDD-based test scenarios. Contribute to AI-related development and implementation. Collaborate with QA and other teams to ensure application quality, performance, and timely delivery.

Mandatory Skills: Java, Cucumber, AI experience of at least 1 year.

Experience: 6+ years in Java development, with relevant Cucumber experience and 1 year of AI experience.

About Us: The Next Generation of WealthTech  

We're Cambridge Wealth, an award-winning force in mutual fund distribution and Fintech. We're not just moving money; we're redefining wealth management for everyone from retail investors to ultra-HNIs (including the NRI segment). Our brand is synonymous with excellence, backed by accolades from the BSE and top Mutual Fund houses.

If you thrive on building high-performance, scalable systems that drive real-world financial impact, you'll feel right at home. Join us in Pune to build the future of finance.

[Learn more: www.cambridgewealth.in]

The Role: Engineering Meets Team Meets Customer

We're looking for an experienced, hands-on Tech Catalyst to accelerate our product innovation. This isn't just a coding job; it's a chance to blend deep backend expertise with product strategy. You will be the engine driving rapid, data-driven product experiments, leveraging AI and Machine Learning to create smart, personalized financial solutions. You'll lead by example, mentoring a small, dedicated team and ensuring technical excellence and rapid deployment in the high-stakes financial domain.

Key Impact Areas: Ship Fast, Break Ground  

1. Backend & AI/ML Innovation  

• Rapid Prototyping: Design and execute quick, iterative experiments to validate new features and market hypotheses, moving from concept to production in days, not months.
• AI-Powered Features: Build scalable Python-based backend services that integrate AI/ML models to enhance customer profiling, portfolio recommendation, and risk analysis.
• System Architecture: Own the performance, stability, and scalability of our core fintech platform, implementing best practices in modern backend development.

2. Product Leadership & Execution  

• Agile Catalyst: Drive and optimize Agile sprints, ensuring clear technical milestones, efficient resource allocation, backlog grooming and maintaining a laser focus on preventing scope creep.
• Mentorship & Management: Provide technical guidance and mentorship to a team of developers, fostering a culture of high performance, code quality, and continuous learning.
• Domain Alignment: Translate complex financial requirements and market insights into precise, actionable technical specifications and seamless user stories.
• Problem Solver: Proactively identify and resolve technical and process bottlenecks, acting as the ultimate problem solver for the engineering and product teams.

3. Financial Domain Expertise  

• High-Value Delivery: Apply deep knowledge of the mutual fund and broader fintech landscape to inform product decisions, ensuring our solutions are compliant, competitive, and truly valuable to our clients.
• Risk & Security: Proactively architect solutions with security and financial risk management baked in from the ground up, protecting client data and assets.

Your Tech Stack & Experience  

The Must-Haves  

• Mindset: A verifiable track record as a proactive First Principle Problem Solver with an intense Passion to Ship production-ready features frequently.
• Startup Experience: Familiarity with the pace and intensity of a fast-iterating environment.
• Customer Empathy: Keeps the customer's experience in mind at all times.
• Team Leadership: Experience in leading, mentoring, or managing a small development team, driving technical excellence and project delivery.
• Systems Thinker: Diagnoses and solves problems by viewing the organization as an interconnected system to anticipate broad impacts and develop holistic, strategic solutions.
• Backend Powerhouse: 2+ years of professional experience with a strong focus on backend development.
• Python Guru: Expert proficiency in Python and related frameworks (e.g., Django, Flask) for building robust, scalable APIs and services.
• AI/ML Integration: Proven ability to leverage and integrate AI/ML models into production-level applications.
• Data Driven: Expert in SQL for complex data querying, analysis, and ETL processes.
• Financial Domain Acumen:Strong, demonstrable knowledge of financial products, especially mutual funds, wealth management, and key fintech metrics.

Nice-to-Haves  

• Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Familiarity with Zoho Analytics, Zoho CRM and Zoho Deluge
• Familiarity with modern data analysis tools and visualization platforms (e.g., Mixpanel, Tableau, or custom dashboard tools).
• Understanding of Mutual Fund, AIF, PMS operations

Ready to Own the Backend and Shape Finance?  

This is where your code meets the capital market. If you’re a Fintech-savvy Python expert ready to lead a team and build a scalable platform in Pune, we want to talk.

Apply now to join our award-winning, forward-thinking team.

Our High-Velocity Hiring Process:  

• You Apply & Engage: Quick application and a few insightful questions. (5 min)
• Online Tech Challenge: Prove your tech mettle. (90 min)
• People Sync: A focused call to understand if there is cultural and value alignment. (30 min)
• Deep Dive Technical Interview: Discuss architecture and projects with our senior engineers. (1 hour)
• Founder's Vision Interview: Meet the leadership and discuss your impact. (1 hour)
• Offer & Onboarding: Reference and BGV check follow the successful offer.

What are you building right now that you're most proud of?

We are looking for motivated and enthusiastic candidates for a Python and Machine Learning Internship. This internship provides an opportunity to gain practical experience in Python programming, data analysis, and machine learning through hands-on projects and industry-oriented tasks.

Key Responsibilities:

• Develop and implement Python-based applications and solutions.
• Work with data preprocessing, analysis, and visualization.
• Build, train, and evaluate machine learning models.
• Apply machine learning algorithms to real-world datasets.
• Assist in developing and testing project modules.
• Debug code and improve model performance.
• Work on practical projects under the guidance of experienced trainers/mentors.

Who Can Apply:

Pursuing students from BE/BTech, BCA, BSc, MCA, MTech, MSc, Diploma and related computer science backgrounds.

Mode of internship: Offline / Online / Hybrid

What You Will Gain:

• Hands-on experience with Python and Machine Learning.
• Practical project development experience.
• Exposure to real-world datasets and ML applications.
• Guidance from experienced mentors.
• Internship Certificate and Letter of Recommendation upon successful completion.

GEMBA CONCEPTS

Experience: ~3–5 years Type: Full-time

AI/ML Engineer

Location: Bengaluru, India (Hybrid)

About Gemba Concepts

Gemba Concepts is a lean manufacturing and technology consulting firm helping clients across pharma, manufacturing, and logistics

modernize how they operate. We build production systems that sit close to the shop floor — warehouse management, manufacturing

traceability, and an applied AI/ML platform whose flagship use cases are visual quality inspection and predictive maintenance. We’re a

tight engineering team that ships real systems for demanding, often regulated, environments.

The Role

We’re looking for an AI/ML Engineer to take ML capabilities from prototype to production. You’ll own models end-to-end — framing the

problem with stakeholders, building and validating the model, and deploying it as a reliable service that holds up against real-world, messy

industrial data. This is a hands-on building role, not a pure research seat: your work goes into client-facing systems.

What You’ll Do

Build and ship computer vision models for visual quality inspection (defect detection, classification, segmentation) that perform under

real factory lighting, throughput, and edge-case conditions.

Develop predictive maintenance models using sensor/time-series data — anomaly detection, remaining-useful-life estimation, failure

prediction.

Own the full ML lifecycle: data pipelines, feature engineering, training, evaluation, and deployment, with proper versioning and monitoring.

Deploy and serve models in production on Azure (AKS), and keep them healthy — track drift, retraining triggers, and latency.

Integrate LLM-based capabilities (we use the Claude API and self-hosted open models) into delivery and product workflows where they

add leverage.

Collaborate with product, engineering, and domain experts to translate fuzzy operational problems into well-scoped ML solutions — and to

know when ML is not the right answer.

Communicate results and limitations clearly to non-ML stakeholders, including clients.

What We’re Looking For

3–5 years of hands-on experience building and deploying ML models in production (not just notebooks or coursework).

Strong Python and the modern ML stack — PyTorch or TensorFlow, scikit-learn, NumPy/Pandas.

Solid grounding in at least one of: computer vision (CNNs, object detection/segmentation, image preprocessing) or time-series /

anomaly detection.

Practical MLOps experience: containerization (Docker), model serving, experiment tracking, and deploying on a cloud platform — Azure /

Kubernetes (AKS) is a strong plus.

Comfort working with imperfect, real-world data — labeling strategy, class imbalance, data drift, and validation that reflects production

reality.

Good engineering hygiene (Git, testing, code review) and the ability to write code others can build on.

Nice to Have

Experience with industrial / manufacturing data or regulated environments (pharma, 21 CFR Part 11 awareness).

Hands-on LLM integration experience — RAG, prompt engineering, working with APIs or self-hosted models (vLLM, Qwen, etc.).

Edge deployment experience (running CV models on-device / near the line).

Exposure to data pipeline tooling and orchestration.

What You’ll Get

Real ownership of ML systems that go into production for serious clients.

A lean, senior-heavy team where you ship fast and learn across the stack.

Direct exposure to applied AI in manufacturing — a domain where the work has tangible, physical impact

AI Architect – Enterprise AI & Generative AI ( Remote) - Contractual

Experience: 8+ years

Location: Remote, India

Employment Type: Contractual

Working Hours: 3:00 PM – 12:00 AM IST

Client Engagement: US-based client

About the Role

We are looking for a hands-on, client-facing AI Architect to lead the architecture, technical direction, and production implementation of enterprise AI and Generative AI solutions for a US-based client.

You should combine strong software engineering and cloud architecture fundamentals with practical experience building secure, scalable, and reliable AI applications. This role involves end-to-end technical ownership, from solution design and prototyping to production deployment and optimization. It is not a research-only, advisory-only, or prompt-writing position.

Key Responsibilities

• Lead end-to-end architecture and delivery of AI/ML and Generative AI solutions, from discovery and proof of concept to production rollout.
• Design LLM-powered applications using Retrieval-Augmented Generation (RAG), embeddings, vector databases, AI agents, and orchestration frameworks.
• Define data ingestion, chunking, retrieval, grounding, evaluation, and knowledge-refresh strategies.
• Select appropriate AI models and platforms based on quality, latency, cost, security, and operational requirements.
• Design cloud-native architectures, APIs, integrations, identity controls, monitoring, and resilient deployment patterns.
• Create architecture diagrams, technical designs, implementation roadmaps, and technical standards.
• Implement AI quality and safety measures, including hallucination mitigation, guardrails, access controls, and prompt-injection defenses.
• Guide engineering, data, ML, and DevOps teams through implementation, code reviews, troubleshooting, and go-live readiness.
• Lead technical discussions and design reviews with US client stakeholders.
• Establish production monitoring, CI/CD, versioning, incident handling, and performance and cost optimization practices.

Required Skills & Qualifications

• 8+ years of relevant experience in software engineering, AI/ML engineering, data engineering, or technology architecture.
• Proven experience personally architecting and deploying at least one enterprise-grade AI or Generative AI solution into production.
• Strong practical knowledge of LLMs, RAG, embeddings, vector databases, orchestration frameworks, and model evaluation.
• Hands-on experience with Python and APIs, including prototyping, code review, and troubleshooting.
• Experience with at least one major cloud platform: Microsoft Azure, AWS, or Google Cloud Platform (GCP).
• Understanding of data pipelines, SQL/NoSQL databases, containers, CI/CD, MLOps, and LLMOps.
• Knowledge of enterprise security, privacy, identity and access management, and responsible AI practices.
• Experience independently leading architecture workshops, design reviews, and technical discussions with US clients.
• Excellent written and spoken English, documentation, and stakeholder management skills.
• Availability to work from 3:00 PM to 12:00 AM IST and willingness to accept a contractual engagement.

Preferred Experience

• Azure AI Foundry, Azure OpenAI, Amazon Bedrock, or Google Vertex AI.
• Azure AI Search or equivalent retrieval tools.
• LangChain, LangGraph, Semantic Kernel, and agent/tool orchestration.
• Model fine-tuning, predictive ML, document intelligence, multimodal AI, or real-time AI applications.
• Experience in cloud modernization, enterprise integrations, data analytics, or technology consulting.
• Relevant AI/cloud architecture certifications and experience mentoring technical teams.

What Success Looks Like

• Designing secure, scalable, production-ready AI solutions rather than demos or conceptual presentations.
• Making sound technical decisions around feasibility, performance, reliability, cost, and AI risks.
• Taking ownership of architecture decisions and resolving technical blockers with limited supervision.
• Building trust with senior US stakeholders and collaborating effectively with India-based delivery teams.

Contract Details

• Initial engagement: Minimum 3 months, with extension subject to project requirements and performance.
• Working schedule: 3:00 PM – 12:00 AM IST.
• Potential permanent employment: After 18 months, the client may consider direct permanent employment at its sole discretion. Conversion is not guaranteed.

Mandatory Screening Criteria

Candidates should be able to:

• Explain an enterprise AI/GenAI solution they personally architected and deployed, including architecture, scale, evaluation, and business impact.
• Whiteboard an enterprise RAG or agentic solution covering data flow, security, authentication, authorization, monitoring, and cost controls.
• Discuss real-world model-quality issues, hallucinations, privacy risks, incidents, and architectural trade-offs.
• Lead a technical design review with US client stakeholders.
• Confirm acceptance of the working hours and contractual engagement.

Apply only if you have hands-on experience architecting and deploying enterprise AI/GenAI solutions in production.

Job Summary 

A Senior Full Stack Observability Engineer – AI Ops & Autonomous Observability to handle / manage , Design and implement enterprise-wide observability across applications, infrastructure, cloud, network, database and digital services. Lead adoption of Datadog, AI Ops, SRE and Autonomous Observability. Build agentic AI and multi-agent orchestration solutions that automate operational workflows and enable autonomous operations.

Technical Skills

• Datadog (Expert)
• OpenTelemetry
• Distributed Tracing
• Application Performance Monitoring
• AI Ops
• LangGraph/CrewAI/AutoGen/Semantic Kernel
• Python
• Azure, AWS
• Kubernetes
• ServiceNow Integration
• 
  

Enterprise Observability Strategy

• Define observability standards, governance and operating model.
• Create monitoring roadmaps, service health frameworks, business service dashboards and dependency mapping.
• 
  

Full Stack Observability Scope

• Applications, APIs, microservices and user journeys.
• Windows, Linux, VMware, storage and infrastructure platforms.
• Azure and AWS.
• Network monitoring, SQL and NoSQL databases.
• 
  

Datadog Platform

• Deploy and optimize Datadog solutions.
• Build dashboards, monitors, alerts and observability-as-code frameworks.
• Integrate Datadog with ServiceNow, Teams and enterprise tooling.
• New Relic exposure is optional.
• 
  

AI Ops Responsibilities

• Event correlation and noise reduction.
• Predictive analytics and anomaly detection.
• Incident intelligence and root cause analytics.
• Proactive issue identification and MTTR reduction

Job Title: AI Architecture Intern

Company: PGAGI Consultancy Pvt. Ltd.

Location: Remote

Employment Type: Internship

Position Overview

We're at the forefront of creating advanced AI systems, from fully autonomous agents that provide intelligent customer interaction to data analysis tools that offer insightful business solutions. We are seeking enthusiastic interns who are passionate about AI and ready to tackle real-world problems using the latest technologies.

Duration: 6 months

Key Responsibilities:

• AI System Architecture Design: Collaborate with the technical team to design robust, scalable, and high-performance AI system architectures aligned with client requirements.
• Client-Focused Solutions: Analyze and interpret client needs to ensure architectural solutions meet expectations while introducing innovation and efficiency.
• Methodology Development: Assist in the formulation and implementation of best practices, methodologies, and frameworks for sustainable AI system development.
• Technology Stack Selection: Support the evaluation and selection of appropriate tools, technologies, and frameworks tailored to project objectives and future scalability.
• Team Collaboration & Learning: Work alongside experienced AI professionals, contributing to projects while enhancing your knowledge through hands-on involvement.

Requirements:

• Strong understanding of AI concepts, machine learning algorithms, and data structures.
• Familiarity with AI development frameworks (e.g., TensorFlow, PyTorch, Keras).
• Proficiency in programming languages such as Python, Java, or C++.
• Demonstrated interest in system architecture, design thinking, and scalable solutions.
• Up-to-date knowledge of AI trends, tools, and technologies.
• Ability to work independently and collaboratively in a remote team environment

Perks:

- Hands-on experience with real AI projects.

- Mentoring from industry experts.

- A collaborative, innovative and flexible work environment

Compensation:

- Stipend: Base is INR 8000/- & can increase up to 20000/- depending upon performance matrix.

After completion of the internship period, there is a chance to get a full-time opportunity as an AI/ML engineer.

Preferred Experience:

• Prior experience in roles such as AI Solution Architect, ML Architect, Data Science Architect, or AI/ML intern.
• Exposure to AI-driven startups or fast-paced technology environments.
• Proven ability to operate in dynamic roles requiring agility, adaptability, and initiative.

About the Role

We are looking for a Junior React Native Developer with 1–2 years of hands-on experience in developing mobile applications using React Native for both Android and iOS platforms.

The ideal candidate should have strong fundamentals in React Native and JavaScript/TypeScript, along with exposure to AI/ML technologies and AI-assisted development tools such as Claude, Cursor, GitHub Copilot, ChatGPT, or similar tools.

This is a Junior-level role for someone who is eager to build mobile applications, learn new technologies, and work on AI-enabled features.

Key Responsibilities

• Develop, maintain, and enhance cross-platform mobile applications using React Native.
• Build and support applications for both Android and iOS platforms.
• Develop reusable, scalable, and user-friendly mobile UI components.
• Integrate REST APIs, third-party APIs, SDKs, and backend services.
• Debug and troubleshoot applications across Android and iOS devices.
• Assist with application testing, performance optimization, and bug fixing.
• Assist in integrating AI/ML or GenAI-powered features into mobile applications.
• Use AI-assisted development tools such as Claude, Cursor, GitHub Copilot, ChatGPT, or similar tools for development, debugging, code generation, and problem-solving.
• Collaborate with the development team to understand requirements and deliver features.
• Participate in code reviews and follow development best practices.

Required Skills

• 1–2 years of hands-on experience with React Native.
• Good hands on experience with JavaScript / TypeScript.
• Understanding of React Native components, hooks, navigation, and state management.
• Experience with REST APIs, JSON, and API integration.
• Exposure to Android and iOS application development.
• Familiarity with Android Studio and Xcode.
• Experience with mobile application debugging and testing.
• Exposure to AI/ML, GenAI, AI APIs, or AI-powered application features.
• Familiarity with AI-assisted development tools such as Claude, Cursor, GitHub Copilot, ChatGPT, or similar tools.
• Good problem-solving and communication skills.

Educational Qualification

B.Tech / B.E. in CSE, IT, or AI/ML only.

Preferred Qualifications

• Candidates currently based in Hyderabad are preferred.
• Immediate Joiners or candidates with a short notice period are preferred.
• Exposure to AI/ML or GenAI projects is an advantage.
• Experience integrating AI APIs into mobile applications is a plus.
• Knowledge of Git/GitHub and basic CI/CD concepts is preferred.
• Experience with Google Play Store / Apple App Store deployment is an added advantage.

Work Location

Gachibowli, Hyderabad

Note: This is a Junior React Native Developer position. Candidates with 1–2 years of relevant React Native experience, Android/iOS exposure, and AI/ML exposure are encouraged to apply.

Responsibilities:

• Architect, develop, and maintain backend components for our Risk Decisioning Platform.
• Build and orchestrate scalable backend services that automate, optimize, and monitor high-value credit and risk decisions in real time.
• Integrate with ORM layers - such as SQLAlchemy - and multi-RDBMS solutions (Postgres, MySQL, Oracle, MSSQL, etc. ) to ensure data integrity, scalability, and compliance.
• Collaborate closely with Product Team, Data Scientists, QA Teams to create extensible APIs, workflow automation, and AI governance features.
• Architect workflows for privacy, auditability, versioned traceability, and role-based access control, ensuring adherence to regulatory frameworks.
• Take ownership from requirements to deployment, seeing your code deliver real impact in the lives of customers and end users.

Requirements:

• Proficiency in Python, SQLAlchemy (or similar ORM), and SQL databases.
• Experience developing and maintaining scalable backend services, including API, data orchestration, ML workflows, and workflow automation.
• Solid understanding of data modeling, distributed systems, and backend architecture for regulated environments.
• Curiosity and drive to work at the intersection of AI/ML, fintech, and regulatory technology.
• Experience mentoring and guiding junior developers.

Technical Skills:

• Languages: Python 3.9+, SQL, JavaScript/TypeScript, Angular.
• Frameworks: Flask, SQLAlchemy, Celery, Marshmallow, Apache Spark.
• Databases: PostgreSQL, Oracle, SQL Server, Redis.
• Tools: pytest, Docker, Git, Nx.
• Cloud: Experience with AWS, Azure, or GCP preferred.
• Monitoring: Familiarity with OpenTelemetry and logging frameworks.

Full Stack Developer – AI/ML & GenAI

Location: Mumbai

Experience: 7–9 Years

Employment Type: Full-time

Virtual Drive: Saturday

We are looking for a Python Full Stack Developer with 3+ years of hands-on experience in AI/ML and Generative AI.

Key Skills: Python Full Stack, FastAPI/Flask/Django, React/Angular, AI/ML, GenAI, LLMs, RAG, Embeddings, Vector Databases, Cloud (AWS/Azure/GCP), CI/CD, Docker & Kubernetes.

Role Overview

We are looking for an experienced Data Scientist – Agentic AI with strong expertise in Python, Machine Learning, Generative AI, Large Language Models (LLMs), RAG and Agentic AI.

The ideal candidate should have hands-on experience in developing, fine-tuning, evaluating and deploying machine learning and GenAI solutions. The candidate should be comfortable working with open-source LLMs, LangChain/LangGraph, PySpark and AI observability/tracing frameworks.

The role involves building intelligent AI systems that can reason, use tools, retrieve information and execute multi-step tasks using Agentic AI architectures.

Mandatory Technical Skills

1. Data Science & Python

• 5+ years of experience in Data Science / Machine Learning / AI.
• Strong programming experience in Python.
• Strong understanding of data analysis, feature engineering and statistical techniques.
• Experience with Python ML and data science libraries such as:
• NumPy
• Pandas
• Scikit-learn
• Matplotlib / Seaborn
• Good understanding of data preprocessing, exploratory data analysis and experimentation.

2. Machine Learning & Statistics

• Strong understanding of Machine Learning fundamentals.
• Experience with supervised and unsupervised learning techniques.
• Knowledge of:
• Regression
• Classification
• Clustering
• Feature Engineering
• Model Selection
• Hyperparameter Tuning
• Cross-validation
• Strong understanding of Statistics / ML fundamentals.
• Ability to interpret model performance and statistical results.

3. Generative AI / LLM

• Strong hands-on experience with Generative AI and Large Language Models (LLMs).
• Understanding of Transformer architecture and modern LLM-based applications.
• Experience working with commercial or open-source LLMs.
• Strong understanding of:
• Prompt Engineering
• Context Management
• Embeddings
• Tokenization
• LLM inference
• Hallucination mitigation

4. RAG – Retrieval Augmented Generation

• Strong hands-on experience developing RAG applications.
• Experience with:
• Document ingestion
• Chunking
• Embeddings
• Vector search
• Semantic search
• Retrieval pipelines
• Context retrieval
• Reranking
• Ability to optimize RAG pipelines for relevance, accuracy and latency.
• Experience integrating LLMs with enterprise knowledge sources.

5. Agentic AI

• Hands-on experience building Agentic AI / AI Agent solutions.
• Understanding of agent architecture and multi-step reasoning workflows.
• Experience with:
• AI Agents
• Multi-Agent systems
• Tool Calling
• Function Calling
• Agent orchestration
• Planning and reasoning workflows
• Memory
• Workflow automation
• Ability to build agents that can interact with tools, APIs, databases and external systems.

6. LangChain / LangGraph

• Strong hands-on experience with LangChain and/or LangGraph.
• Experience building LLM workflows and agent-based applications.
• Understanding of:
• Chains
• Agents
• Tools
• State management
• Graph-based workflows
• Agent orchestration
• Retrieval workflows
• Experience designing scalable Agentic AI workflows.

7. LLM Fine-Tuning

• Hands-on experience with LLM fine-tuning.
• Understanding of techniques such as:
• Supervised Fine-Tuning (SFT)
• Parameter-Efficient Fine-Tuning
• LoRA
• QLoRA
• Experience preparing datasets for fine-tuning.
• Ability to evaluate fine-tuned models against baseline models.
• Understanding of model optimization and inference considerations.

8. BERT / LLaMA / Open-Source LLMs

Experience working with one or more open-source / transformer-based models such as:

• BERT
• LLaMA / Llama
• Mistral
• Gemma
• Qwen
• Other open-source LLMs

Candidate should understand model loading, inference, fine-tuning and evaluation.

9. PySpark

• Strong experience with PySpark for large-scale data processing.
• Experience working with large datasets and distributed data processing.
• Knowledge of:
• Data transformations
• Data cleaning
• Aggregations
• Joins
• Spark SQL
• Performance optimization
• Ability to build scalable data processing pipelines.

10. Model Validation & Evaluation

• Experience validating and evaluating ML and GenAI models.
• Understanding of traditional ML evaluation metrics.
• Experience evaluating LLM/RAG applications using relevant quality metrics.
• Ability to compare model performance and identify areas for improvement.
• Experience with:
• Accuracy
• Precision
• Recall
• F1 Score
• ROC-AUC
• Retrieval metrics
• LLM response quality
• Groundedness / relevance
• Experience designing evaluation datasets and test cases is preferred.

11. AI Tracing / Observability

• Experience with AI/LLM tracing and observability.
• Ability to monitor AI applications in production.
• Experience tracking:
• LLM requests/responses
• Latency
• Token usage
• Errors
• Retrieval performance
• Agent/tool execution
• Model performance
• Exposure to tools/frameworks such as LangSmith, OpenTelemetry, Arize Phoenix, MLflow or similar is preferred.

12. Model Deployment

• Experience deploying ML/LLM/GenAI solutions into production.
• Exposure to cloud and/or on-premise model deployment.
• Experience with model serving, APIs and production inference.
• Knowledge of deployment environments such as:
• AWS
• Azure
• GCP
• On-premise infrastructure
• Experience with Docker, APIs and CI/CD is an advantage.

Key Responsibilities

• Design, develop and deploy Data Science, Machine Learning and GenAI solutions.
• Build production-ready RAG and Agentic AI applications.
• Develop intelligent agents capable of tool calling, reasoning and multi-step task execution.
• Build LLM-powered applications using LangChain/LangGraph.
• Work with open-source LLMs including BERT, LLaMA and other transformer-based models.
• Fine-tune LLMs for specific business use cases.
• Develop scalable data processing pipelines using PySpark.
• Perform data analysis, feature engineering and statistical modeling.
• Develop and maintain model validation and evaluation frameworks.
• Evaluate ML and LLM models using appropriate performance and quality metrics.
• Implement AI tracing, monitoring and observability for production GenAI systems.
• Deploy models and AI applications in cloud or on-premise environments.
• Optimize model performance, response quality, latency and cost.
• Troubleshoot issues related to model inference, retrieval, agents and LLM workflows.
• Collaborate with Data Scientists, ML Engineers, Software Engineers and business stakeholders.
• Convert business requirements into scalable AI/ML solutions.

Good to Have

• Experience with Vector Databases such as:
• FAISS
• Pinecone
• Weaviate
• Milvus
• Chroma
• Azure AI Search
• Experience with MLflow or similar ML lifecycle tools.
• Experience with Docker/Kubernetes.
• Experience with REST APIs / FastAPI.
• Knowledge of cloud AI/ML services.
• Experience with MLOps / LLMOps.
• Experience with multi-agent frameworks other than LangChain/LangGraph.
• Experience working with enterprise GenAI applications.

Ideal Candidate Profile

The ideal candidate should be a Data Scientist / ML Engineer with strong GenAI and Agentic AI experience, rather than a pure Python developer.

A strong candidate would typically have:

Data Science + Python + ML + Statistics + GenAI/LLM + RAG + Agentic AI + LangChain/LangGraph + LLM Fine-Tuning + Open-Source LLMs + PySpark + Model Evaluation + AI Observability + Model Deployment.

Core Mandatory Skills

Data Science, Python, Machine Learning, Statistics/ML Fundamentals, GenAI/LLM, RAG, Agentic AI, LangChain/LangGraph, LLM Fine-Tuning, BERT/LLaMA/Open-Source LLMs, PySpark, Model Validation/Evaluation, AI Tracing/Observability, Cloud/On-Prem Model Deployment.

🚀 WE’RE HIRING | AI/ML GENERATIVE AI ENGINEER

📍 Location: Remote

💼 Experience: 5+ Years

🔄 Position: Contract – Extendable

🔹 ROLE HIGHLIGHTS

➤ Build and deploy AI/ML amp; Generative AI solutions

➤ Develop LLM, RAG, NLP, Recommendation amp; Predictive solutions

➤ Work on AI Agents, Chatbots, Computer Vision amp; Content Intelligence

➤ Build ML models using PyTorch, TensorFlow, Keras amp; Scikit-learn

➤ Develop RAG solutions using LangChain, LlamaIndex, FAISS/Milvus

➤ Integrate OpenAI, Azure OpenAI, AWS Bedrock, Vertex AI amp; Hugging Face

➤ Build scalable AI APIs using Python, FastAPI/Flask/Django

➤ Contribute to Private AI amp; Smart Agentic Systems

⚙️ MUST-HAVE SKILLS

◆ Python – 3+ years

◆ AI/ML – 5+ years

◆ Generative AI – 2+ years

◆ LLMs, RAG, Embeddings , Transformers

◆ ML/DL, NLP amp; Predictive Analytics

◆ Cloud AI Platforms – Azure / AWS / GCP

◆ AI/ML Deployment | MLOps

Interview Process - F2F Round at Pune Location

Key Responsibilities

 Strong understanding of Machine Learning algorithms (supervised and unsupervised)

 Hands-on experience with Deep Learning frameworks (TensorFlow, PyTorch, or similar)

 Experience in NLP techniques and libraries (NLTK, spaCy, Hugging Face, etc.)

 Solid knowledge of Statistics, probability, and data analysis methods

 Proficiency in SQL for querying relational databases

 Strong programming skills in Python (or similar languages)

 Excellent communication skills with the ability to explain complex concepts simply

Required Qualifications

 3+ years of hands-on experience as a Data Scientist or in a similar role.

 Strong expertise in classical machine learning and regression modeling.

 Solid understanding of statistics, including probability, distributions, hypothesis testing,

and correlation analysis.

 Proficiency in Python with libraries such as: scikit-learn pandas NumPy

 Experience working with structured/tabular data.

 Strong problem-solving and analytical thinking skills.

 Ability to clearly explain models and results to non-technical stakeholders.

• Bachelor’s degree in computer science, Data Science, Information Systems, or a related field  
• 8-10 years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering  
• Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure  
• Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn  

We are looking for a Data Science & Machine Learning Senior Associate with 3–5 years of relevant experience in data science, machine learning, and analytics. The candidate will be responsible for developing predictive models, analyzing complex datasets, building scalable ML solutions, and supporting production-grade data science applications on cloud platforms.

• Develop and implement machine learning and predictive analytics models.
• Perform data analysis, statistical modeling, and optimization to solve business problems.
• Build demand forecasting and predictive models using time-series and other advanced techniques.
• Work with large datasets using Python, SQL, PySpark, and cloud-based data platforms.
• Develop and maintain scalable data pipelines for ML model development and deployment.
• Implement MLOps practices including model deployment, monitoring, retraining, and data-drift detection.
• Collaborate with software engineers, product teams, and business stakeholders to convert business requirements into analytical solutions.
• Validate models for accuracy, robustness, bias, and production readiness.
• Create meaningful visualizations and communicate analytical insights to technical and non-technical stakeholders.

• Python
• SQL
• Data Science & Machine Learning
• Predictive Modeling
• Statistical Analysis
• Machine Learning Algorithms
• Optimization Techniques
• Google Cloud Platform (GCP)
• BigQuery
• Dataflow
• Dataproc
• Data Fusion
• Cloud SQL
• Airflow
• PySpark
• PostgreSQL
• Terraform
• Tekton
• APIs
• MLOps

• Java

• Demand Forecasting
• Time-Series Analysis
• Neural Networks
• Ensemble Methods
• Support Vector Machines (SVM)
• Regression and Cluster Analysis
• ML Model Testing
• Bias Detection and Data Drift Monitoring
• Production ML Deployment
• QlikSense
• Automotive or Supply Chain Analytics

Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, Mathematics, or a related technical field.Master's degree in a relevant quantitative or technical field is preferred.

Lead Developer – Java, React, Node.js & AI/ML

Location: Mumbai

Experience: 8–10 Years

Job Type: Full-time

Job Description

We are looking for a Lead Developer with 8–10 years of experience in software development, with strong expertise in Java, React, and Node.js. The candidate should have at least 1 year of hands-on professional experience in Artificial Intelligence and Machine Learning and experience in building modern, scalable technology solutions.

Key Skills

Java, React, Node.js, AI/ML, MongoDB, PostgreSQL, Key-Value Caching, Vector Databases, ChromaDB, pgvector

Candidate should have strong software development experience along with good knowledge of modern databases, caching technologies, and vector search solutions.

Greetings!

Hiring For Large Product Based Company!

Role- Mlops Engineer

Experience- 8-12 years

Location- Pune, Nagpur

JD-

• 8-10 years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering  
• Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure  

Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn  

This is a remote position.

About Leegality:

Leegality works with large Indian businesses to digitally transform critical compliance processes in a fast, easy and secure way.

We have multiple products across 2 categories:

Document Infrastructure:

Products that help businesses build paperless processes at scale:

1. Document Execution Workflow: A unified platform for businesses to digitally execute (eSign, eStamp, Template Pre-fill, Document Fraud Prevention etc.) agreements, forms and other documents in a compliant way. Currently in use by 2000+ Indian businesses from giants like HDFC and SBI Cards to high-growth disruptors like goDigit and Cars24.
2. Contract Management: An AI-powered platform for businesses to quickly review, negotiate and take action on contract
3. Signstation: A simple platform for businesses to digitally sign simple documents like invoices, policies and letters in a cost effective manner

Consent Infrastructure:

1. Consentin: An end-to-end DPDP and Privacy compliance platform for Indian businesses
2. Consentin Lens: A data discovery platform for businesses to identify the personal data they collect and store.

If you’re interested in building mission critical software that operates at population scale (75 million + Indians have signed at least one document through Leegality) then join Leegality.

Curious about our impact? Explore our customer success stories: leegality.com/case-studies

Our Culture

At Leegality, trust, ownership, transparency, and having fun while doing meaningful work are core to how we operate — not just values on paper. Our team rated us an incredible 97 eNPS for FY 2023–24 — the highest among 175+ startups surveyed.

We focus deeply on helping our people grow and stay motivated. Some of the perks you’ll enjoy:

• Flexible working hours
• Hybrid work setup
• Bi-annual performance appraisals
• A culture that rewards initiative, curiosity, and impact

If you're looking for a place where you can make a real difference while working with smart, driven, and genuinely nice people, welcome to Leegality.

Location: Hybrid

Job Brief:

• As a Machine Learning Engineer specializing in Computer Vision (CV) and Natural Language Processing (NLP), you will develop solutions to interesting technical problems, exploring exciting growth opportunities and having a real impact on our product, particularly focusing on document and content intelligence.
• To ensure success, you should demonstrate solid data science knowledge and experience in a related ML, CV, or NLP role. A first-class engineer will be someone whose expertise enhances our systems for document intelligence and content processing

Responsibilities:

• Designing machine learning systems, self-running artificial intelligence (AI) software, and specialized models for Computer Vision and Natural Language Processing applications.
• Transforming data science prototypes and applying appropriate deep learning algorithms and tools to text and image/document data.
• Solving complex CV and NLP problems with multi-layered data types, such as image/document classification, information extraction, semantic search, and object detection.
• Optimizing existing machine learning models, with a focus on high-performance model deployment for CV and NLP tasks.
• Developing ML algorithms (including large language models/LLMs and computer vision models) to analyze huge volumes of historical text, image, and document data to make predictions and automate workflows.
• Running tests, performing statistical analysis, and interpreting test results for CV/NLP model performance.
• Documenting machine learning processes, model architectures, and data pipelines.
• Keeping abreast of developments in machine learning, Computer Vision, and Natural Language Processing.

Requirements:

• 3+ years of relevant experience in Machine Learning Engineering, with a strong focus on Computer Vision and/or Natural Language Processing.
• Advanced proficiency with Python.
• Extensive knowledge of ML frameworks, libraries (e.g., PyTorch, Transformers), data structures, data modeling, and software architecture.
• Experience with building and maintaining scalable RESTful APIs (e.g., FastAPI).
• In-depth knowledge of mathematics, statistics, deep learning (CNNs, RNNs, Transformers), and algorithms.
• Superb analytical and problem-solving abilities, especially for unstructured data challenges.
• Great communication and collaboration skills.
• Excellent time management and organizational abilities.
• Experience with cloud platforms (e.g., AWS) for model deployment and MLOps.

Recruitment Process:

• Our hiring process combines AI-powered evaluations with structured interviews to ensure a fair and seamless experience.
• You will be contacted via email with the next steps upon being shortlisted.
• The process may include Assessments, AI-enabled interviews, and In-Person Interviews with our team.
• Final selection and CTC will be based on your overall performance and experience.

Apply directly through our career page: https://careers.leegality.com/jobs/Careers

For more information about us please visit our:

Our Company and Culture: https://bit.ly/3Iqm5SB

Our Website: www.leegality.com/

Our LinkedIn Page: www.linkedin.com/company/leegality/

Leegality's Privacy Notice: https://www.leegality.com/employee-privacy-notice

Experience: 0–1 Year / Freshers

Location: Remote

Employment Type: Full-Time

About the Role

We are looking for a Junior Software Engineer – AI/ML with hands-on academic, internship, or personal project experience in Core AI/ML.

Key Areas We’re Looking For

• Core AI/ML
• Trading / Finance
• CCTV / Video Analytics
• Face Recognition
• OMR / OCR

Required Skills

• Python and basic Machine Learning concepts
• Experience with AI/ML projects
• Knowledge of OpenCV, Scikit-learn, PyTorch, or TensorFlow
• Basic understanding of model training, evaluation, and data preprocessing
• Git/GitHub

Candidate Profile

• Freshers or candidates with 0–1 year experience
• Practical AI/ML projects preferred
• Should be able to clearly explain their project, model/algorithm, dataset, and individual contribution

Job Description:

We are seeking a versatile and highly skilled Lead AI/ML Engineer with deep expertise in Generative AI (GenAI) and Large Language Models (LLMs). This role requires a leader who can take full ownership of the

AI lifecycle—from initial architectural design to final production execution. You will lead the development of scalable AI-powered applications, demonstrating exceptional execution skills and the ability to deliver high-performance results under pressure in demanding production environments.

Machine Learning & LLM Capability:

 End-to-End ML Engineering: Build and manage comprehensive ML pipelines, including data ingestion, preprocessing, training, and evaluation using frameworks like PyTorch, TensorFlow, and Scikit-learn.  Advanced LLM Systems: Design and implement sophisticated LLM-based applications such as autonomous agents, chatbots, and complex automation tools.

 Generative AI Specialization: Architect and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases like FAISS, Pinecone, or Weaviate.

 Model Optimization: Fine-tune open-source and proprietary models (e.g., LLaMA, GPT) using advanced techniques like LoRA, QLoRA, or instruction tuning.

 Agentic Frameworks: Develop complex agentic workflows utilizing frameworks such as LangChain or LlamaIndex.

 Prompt Engineering: Implement expert-level prompt engineering, tool/function calling, and structured output generation.

 Project Ownership & Execution

 Full Lifecycle Ownership: Take complete accountability for the full ML and GenAI lifecycle, spanning data processing, model development, monitoring, and optimization.

 Architectural Leadership: Drive strategic architectural decisions for AI platforms, ensuring they are modular, scalable, and maintainable.

 Execution Excellence: Write clean, high-performance Python code following strict OOP principles and manage CI/CD pipelines for seamless project execution.

 Leadership & Mentoring: Act as a key technical leader, managing stakeholders and mentoring team members to ensure all project milestones are met with quality.

 System Integrity: Manage model and prompt versioning, experiment tracking, and comprehensive documentation for all pipelines and workflows.

 Performance Under Pressure

 Production Reliability: Ensure all AI systems maintain extreme scalability and performance under heavy production workloads, including both batch and real-time processing.

 High-Pressure Optimization: Rapidly optimize inference latency and system costs for ML and LLM systems to meet urgent business and technical requirements.

 Proactive Problem Solving: Apply strong analytical thinking to address complex challenges such as system drift, hallucinations, and latency in fast-paced environments.

 Robust Guardrails: Implement and manage strict evaluation frameworks and feedback loops to maintain system quality under stress.

Qualifications:

 Bachelor’s or Master’s degree in Computer Science, AI, ML, or a related field.

 Proven expertise in Python, system design, and scalable AI/ML architecture.

 Deep knowledge of NLP, Computer Vision, and Deep Learning models.

 Hands-on experience with Docker, Kubernetes, MLOps, and major cloud platforms (AWS, GCP, or Azure).

Bachelor’s degree in Engineering, Computer Science, or a related discipline

• 8+ years of experience in project or programme management

• 3+ years of experience delivering AI, Machine Learning, Data, or Analytics programmes

• PMP, Agile, Scrum, or equivalent certification preferred

Title                                 : Senior AI Platform / MLOps Engineer

Experience                    : 6+ years

Work type                      : Chennai - Work from Office/other locations - Remote

Employment Type      : Full Time

Notice Period              : Immediate

Work Day                     :Mon to Fri

Key Responsibilities:

• Install, configure and operate OpenShift, NVIDIA GPU operator, OpenShift AI, and NIM microservices on 12× RTX PRO 6000 across two servers; single-node and HA control-plane topologies
• Serving configuration and tuning: quantized model deployment (FP8/FP4), replica balancing, batching, KV-cache and context management
• Azure GPU build environments: provisioning, cost control, parity with the on-prem stack via pinned container/model versions; cloud-to-factory migration with parity regression
• GitOps CI/CD, container registry, artifact/model versioning, environment promotion; observability and audit wiring (Splunk, Prometheus/Grafana)
• Benchmark automation: load harness, p50/p95/p99 latency, tokens/sec, GPU utilization; the capacity report data pipeline
• Platform upgrade procedure with evaluation-regression gates; deployment runbook as a first-class deliverable

Technical Skills:

• 6+ years infrastructure/platform engineering with 3+ years production Kubernetes; OpenShift experience strongly preferred
• Hands-on GPU inference serving in production: NIM, Triton, vLLM, or TensorRT-LLM — you have sized, deployed, and tuned LLM serving on real GPUs and can talk memory-bandwidth trade-offs
• GitOps fluency (ArgoCD/Flux), infrastructure-as-code, container internals; comfortable in air-gapped/proxy-restricted enterprise networks
• Observability depth: metrics, traces, log pipelines; has built performance test harnesses, not just run them

• Azure or AWS GPU compute operations experience

Strongly preferred

• NVIDIA GPU operator and AI Enterprise stack specifics; KServe; Milvus or pgvector operations; VAST/NFS/S3 storage integration; banking or other regulated-environment delivery

About Ampera: 

Ampera Technologies, a purpose driven Digital IT Services with primary focus on supporting our client with their Data, AI / ML, Accessibility and other Digital IT needs. We also ensure that equal opportunities are provided to Persons with Disabilities Talent. Ampera Technologies has its Global Headquarters in Chicago, USA and its Global Delivery Center is based out of Chennai, India. We are actively expanding our Tech Delivery team in Chennai and across India. We offer exciting benefits for our teams, such as 1) Hybrid and Remote work options available, 2) Opportunity to work directly with our Global Enterprise Clients, 3) Opportunity to learn and implement evolving Technologies, 4) Comprehensive healthcare, and 5) Conducive environment for Persons with Disability Talent meeting Physical and Digital Accessibility standards 

PRINCIPAL AI ENGINEER @ METADOME.AI

Company Description

Metadome.ai builds frontier AI models that transform text, drawings, and CAD into production-ready, interactive 3D experiences. The company advances a full generative pipeline—text-to-CAD, 2D-to-3D

reconstruction, CAD completion and harmonization, and real-time interactive rendering—engineered for the precision required in the physical world. Its technology currently powers the modernization of

OEM aftersales for more than 30 automotive and heavy-equipment manufacturers worldwide, delivering accurate, scalable, and fast 3D solutions. Metadome.ai’s platform enables shoppable 3D parts, step-by-step repair animations, and a headless API that feeds consistent 3D assets into commerce, dealer, training, and service systems. The broader mission is to allow anyone to move from an idea, drawing, or specification to a production-grade 3D model and beyond in seconds.

Role Description

As a Principal AI Engineer — Generative CAD & 3D, you will lead the design, development, and deployment of advanced AI models that convert text, 2D drawings, and CAD files into engineering-grade 3D content. You will architect end-to-end generative pipelines, including

text-to-CAD, 2D-to-3D reconstruction, CAD completion, and real-time rendering, collaborating closely with product, design, and engineering teams to ship robust production systems. Day-to-day, you will experiment with novel neural network architectures, optimize model performance on large-scale CAD datasets, write high-quality production code, and guide the integration of AI services into customer-facing platforms. You will mentor other engineers, establish best practices for AI development, and contribute to technical strategy and roadmap. This is a full-time, hybrid role based in Bengaluru, with a mix of on-site collaboration and work-from-home flexibility.

Qualifications

• Strong foundation in Computer Science and Software Development, including data structures, algorithms, system design, and production-grade coding in languages such as Python, C++, or similar.
• Deep expertise in Neural Networks and Pattern Recognition, with hands-on experience designing, training, and deploying modern deep learning architectures for complex, high-dimensional data.
• Experience with Natural Language Processing (NLP), including working with text encoders, multimodal models, and integrating language understanding into generative workflows.
• Advanced degree (Master’s or PhD) in Computer Science, Electrical Engineering, Applied Mathematics, or a related field, or equivalent practical experience in AI/ML research and engineering.
• Background in 3D geometry, CAD, computer graphics, or related domains, with familiarity in 3D representations, mesh processing, and rendering pipelines.

Hi,

Greetings !!

We/re are looking for someone who has Hands-on experience with CV/ML

The location for the same is Bangalore.

Requirements

• 11–14 years total experience
• Computer Vision – strong hands-on experience
• Object Detection – YOLO(Preferred), Faster R-CNN, SSD, etc.
• Image Processing – OpenCV, image enhancement, segmentation, feature extraction
• Machine Learning / Deep Learning – CNNs, model training, evaluation, optimization
• AI/ML – production-level AI solution development
• LLM / GenAI – practical exposure to LLMs, multimodal AI, RAG, VLMs, or GenAI
• Python – strong programming skills
• Model deployment – preferably TensorRT, ONNX, Docker, Kubernetes, cloud, or edge deployment
• Bangalore – candidate should be based in / willing to work from Bangalore

Preferred

• Vision Transformers / ViT
• YOLOv8/YOLOv9/YOLOv10/YOLO11
• PyTorch / TensorFlow
• NLP / LLM / VLM
• Generative AI
• CUDA / GPU optimization
• Edge AI / NVIDIA
• Experience leading CV/AI projects or teams

If interested, Share CV at: snigdhaattheratebeanhr.com

About NonStop io Technologies

NonStop io Technologies is a value-driven company with a strong focus on process-oriented software engineering. We specialize in Product Development and have a decade's worth of experience in building web and mobile applications across various domains. NonStop io Technologies follows core principles that guide its operations and believes in staying invested in a product's vision for the long term. We are a small but proud group of individuals who believe in the 'givers gain' philosophy and strive to provide value in order to seek value. We are committed to and specialize in building cutting-edge technology products and serving as trusted technology partners for startups and enterprises. We pride ourselves on fostering innovation, learning, and community engagement. Join us to work on impactful projects in a collaborative and vibrant environment.

Brief Description:

We're seeking an AI/ML Engineer to join our team. As AI/ML Engineer, you will be responsible for designing, developing, and implementing artificial intelligence (AI) and machine learning (ML) solutions to solve real-world business problems. You will work closely with engineering teams, including software engineers, domain experts, and product managers, to deploy and integrate Applied AI/ML solutions into the products that are being built at NonStop io. Your role will involve researching cutting-edge algorithms and data processing techniques, and implementing scalable solutions to drive innovation and improve the overall user experience.

Responsibilities

● Applied AI/ML engineering; Building engineering solutions on top of the AI/ML tooling available in the industry today. Eg: Engineering APIs around OpenAI

● AI/ML Model Development: Design, develop, and implement machine learning models and algorithms that address specific business challenges, such as natural language processing, computer vision, recommendation systems, anomaly detection, etc.

● Data Preprocessing and Feature Engineering: Cleanse, preprocess, and transform raw data into suitable formats for training and testing AI/ML models. Perform feature engineering to extract relevant features from the data

● Model Training and Evaluation: Train and validate AI/ML models using diverse datasets to achieve optimal performance. Employ appropriate evaluation metrics to assess model accuracy, precision, recall, and other relevant metrics

● Data Visualization: Create clear and insightful data visualizations to aid in understanding data patterns, model behaviour, and performance metrics

● Deployment and Integration: Collaborate with software engineers and DevOps teams to deploy AI/ML models into production environments and integrate them into various applications and systems

● Data Security and Privacy: Ensure compliance with data privacy regulations and implement security measures to protect sensitive information used in AI/ML processes

● Continuous Learning: Stay updated with the latest advancements in AI/ML research, tools, and technologies, and apply them to improve existing models and develop novel solutions

● Documentation: Maintain detailed documentation of the AI/ML development process, including code, models, algorithms, and methodologies for easy understanding and future reference.

Qualifications & Skills

● Bachelor's, Master's, or PhD in Computer Science, Data Science, Machine Learning, or a related field. Advanced degrees or certifications in AI/ML are a plus

● Proven experience as an AI/ML Engineer, Data Scientist, or related role, ideally with a strong portfolio of AI/ML projects

● Proficiency in programming languages commonly used for AI/ML. Preferably Python

● Familiarity with popular AI/ML libraries and frameworks, such as TensorFlow, PyTorch, scikit-learn, etc.

● Familiarity with popular AI/ML Models such as GPT3, GPT4, Llama2, BERT etc.

● Strong understanding of machine learning algorithms, statistics, and data structures

● Experience with data preprocessing, data wrangling, and feature engineering

● Knowledge of deep learning architectures, neural networks, and transfer learning

● Familiarity with cloud platforms and services (e.g., AWS, Azure, Google Cloud) for scalable AI/ML deployment

● Solid understanding of software engineering principles and best practices for writing maintainable and scalable code

● Excellent analytical and problem-solving skills, with the ability to think critically and propose innovative solutions

● Effective communication skills to collaborate with cross-functional teams and present complex technical concepts to non-technical stakeholders

Responsibilities

• Engineering technical lead for the AI Garage team which responsible for developing AI/ML systems and Agentic solutions in HR domain 
• Drive engineering excellence for the AI Garage team 
• Provide technical leadership in regards to immediate and complex technical problems to leads of associated teams.
• Influence and Design and build scalable Agentic systems in HR domain. 
• Collaborate with AI Garage Leads ( Eng Managers, Product Managers and AI strategist ) and stakeholders to understand their needs and translate them into technical solutions.
• Stay up-to-date with the latest trends in Agentic and AIML engineering.

Required Skill :

• Master's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience
• 8+ years of experience in software development, including proficiency in one or more programming languages, artificial intelligence, machine learning algorithms and tools, and Generative AI (Large Language Models, Multi-Model, etc.)
• Design and implement scalable AI solutions for Enterprises.Experience in Building and rolling out AI and agentic solutions for Global Enterprises
• Multi-Agent Systems: Handle collaboration, memory persistence, and dynamic planning using frameworks like LangGraph, CrewAI, or AutoGen.
• Optimization & Evaluation: Track agent trajectories, manage cost/latency, and implement LLM-as-a-judge frameworks (e.g., Phoenix, LangSmith).
• Safety & Compliance: Prompt injection mitigation, sandboxed code execution, and human-in-the-loop (HITL) checkpoints.
• Experience with offline,online and RL based validation
• Metrics Selection: Knowing which evaluation metric matches specific business constraints (e.g., using Precision/Recall or Area Under the ROC Curve (AUC) rather than basic Accuracy on an imbalanced classification dataset).
• Good understanding of the Bias-Variance tradeoff, Overfitting vs. Underfitting, and practical techniques to fix them (L1/L2 Regularization, Dropout, Early Stopping). 
• Dynamic Context Compaction: Skills in designing algorithms that programmatically truncate, summarize, or extract semantic milestones from a running conversation log so the agent doesn't suffer from "lost in the middle" phenomena during long-horizon tasks.
• Advanced Reasoning Frameworks: Implementing and customizing structural execution patterns like ReAct (Reason + Act), Tree of Thoughts (ToT), and Plan-and-Solve loops, giving models the cognitive scaffolding to break down vague goals into deterministic DAGs 
• Evidence in runtime layers that scan user inputs for prompt injection or jailbreak attempts designed to hijack an agent’s system tools, alongside strictly isolating tool execution environments
• Experience with LoRA or SFT and reasoning