AI Transformation Engineer

Strong Senior AI Engineer Profile with production-grade LLM and Python expertise

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Mandatory (Experience 1): Must have at least 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems in real-world environments

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Mandatory (Tech skill 1): Must have strong programming skills in Python and SQL, with hands-on ML/data libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow), plus REST API design

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Mandatory (Experience 2): Must have hands-on experience building and deploying production-grade ML/LLMs including RAG pipelines, document parsing, information extraction, and text processing on large-scale unstructured data (preprocessing, chunking, embeddings, feature engineering)

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Mandatory (Tech skill 2): Must have strong NLP / extraction-focused ML depth — transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.

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Mandatory (Tech skill 3): Must have hands-on experience with AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for model training, fine-tuning, deployment, and inference at scale

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Mandatory (Tech skill 4): Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI)

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Mandatory (Tech skill 5): Must have hands-on experience with MLOps ecosystem including experiment tracking (MLflow, Weights & Biases), model versioning/registry, automated retraining, and CI/CD (GitHub Actions, Azure DevOps, Docker, Kubernetes)

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Mandatory (Tech skill 6): Must have experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement, plus AI observability (Prometheus, Grafana, SLOs).

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Mandatory (Team Leadership): Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges

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Mandatory (Education) - B.Tech or B.E. If graduated in BCA or BCS, they should have a Maters or Post graduation

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Mandatory (Company): B2B Tech/Consulting/B2B Software

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.

The Role

We’re looking for a Senior Applied AI & Data Engineer to become our first dedicated AI and data engineer.

You’ll build conversational AI experiences across web, mobile, and in-store channels while developing the data foundation behind them. You’ll make key technical decisions and own your work through to production.

What You’ll Do

• Build AI assistants using tool calling to work with real product, search, and order systems

• Design guardrails and evaluation sets to ensure AI responses are accurate and safe

• Build real-time and voice-enabled AI experiences

• Improve product data quality through AI-assisted enrichment and review workflows

• Build data pipelines, analytics, and personalisation systems

• Work closely with web and mobile developers and help guide technical implementation

What You’ll Need

• 6+ years of experience building and running production backend systems

• Strong Python skills, plus experience with JavaScript/TypeScript backends

• Experience shipping at least one LLM-powered feature to real users

• Experience with search and relevance

• Experience building data pipelines and analytics stores

• Comfortable deploying and monitoring services on a major cloud platform

• Strong communication skills and the ability to work independently

Nice to Have

• Experience with speech or voice AI

• E-commerce or retail technology experience

• Experience building multilingual products

AI Engineer

LLMs, Agents & AI Services

📍 Mumbai (On-site) | Full-time | 2-4 years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

AI is core to how we design, deliver, and scale software for our customers.

We are hiring an AI Engineer for a dedicated client engagement building a complex production AI platform, working on the AI capabilities and agentic features at the core of the product.

The mandatory requirement for this role is at least one AI feature personally shipped to production for real users, with operational ownership.

The role suits someone who thinks quickly on solutioning, can take an ambiguous problem to a working prototype in days, and has the discipline to carry it through to production with predictable economics.

You will work alongside the Senior AI Engineer and the wider pod, with ownership of parts of the AI surface area of the product.

Responsibilities:

Solutioning and POCs

Translate ambiguous customer problems into working POCs at speed.

Pick the right model, framework, and architecture, and demonstrate value early before scaling investment.

LLM Application Development

Build AI features and services using LLM APIs from OpenAI, Anthropic, Google, and self-hosted open-weight models (Llama, Qwen, Mistral).

Choose the right model per use case based on cost, latency, capability, and context-window trade-offs.

Agentic System Design

Design and implement agentic workflows using LangGraph, CrewAI, AutoGen, LlamaIndex Agents, or custom orchestration.

Cover tool use, planning, memory, and multi-step reasoning appropriate to the problem.

API and Service Development

Build production AI services and APIs using Python and FastAPI.

Handle streaming responses, async processing, structured outputs, retries, and graceful degradation when models or tools fail.

Retrieval and Tool Integration

Implement RAG pipelines with vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma), embeddings, chunking strategies, hybrid search, and reranking.

Integrate external tools, internal APIs, and document sources through tool-calling and MCP-style patterns.

Cost Analysis and Unit Economics

Model the per-request and per-user cost of every AI feature before it ships.

Track token usage, prompt caching, batching, and model-routing strategies.

Drive measurable improvements in unit economics.

Production Hardening

Add observability and tracing (LangSmith, Langfuse, OpenTelemetry), guardrails, content safety checks, prompt injection defences, and fallback behaviour.

Prompt Engineering and Evaluation

Design, test, and iterate prompts with measured outcomes.

Build evaluation harnesses for accuracy, hallucination, latency, and cost.

Run benchmarks across models and prompt variants before locking in a design.

Requirements:

AI Feature Shipped to Production (Mandatory)

Must have personally built and shipped at least one AI feature that runs in production for real users, with operational ownership.

POCs, internal demos, and one-off scripts do not qualify.

2 to 4 Years of Professional Software or AI Engineering Experience

With at least one production AI feature owned end to end.

Strong Python Proficiency and API Development with FastAPI

Comfort with type hints, async, packaging, testing, streaming responses, and authentication.

Production-grade Python, not notebook-only code.

Hands-on Depth Across the LLM and Agent Stack

Working experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or self-hosted open-weight models (vLLM, Ollama, Together, Replicate).

Working familiarity with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.

Working knowledge of RAG, embeddings, and vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma).

Solutioning Speed and POC Velocity

Demonstrated ability to move from a fuzzy problem to a working prototype in days.

Strong instinct for what to build first, what to defer, and what to throw away.

Cost Discipline for Production AI

Ability to calculate, monitor, and optimise the cost of LLM APIs, tokens, embeddings, vector store usage, and infrastructure.

Treats unit economics as a first-class concern.

AWS Familiarity

Working knowledge of EC2, S3, IAM, and at least one of Bedrock, SageMaker, or equivalent.

Comfortable in a Fast-Moving Environment

Self-directed, comfortable with ambiguity, takes ownership without being asked, and ships under shifting priorities.

Strong Written and Spoken English Communication

Able to explain trade-offs to non-AI engineers, designers, product managers, and clients in plain language.

Nice to Have

• fine-tuning or LoRA, QLoRA, PEFT exposure
• MCP server authoring
• eval framework experience (LangSmith, Promptfoo, Ragas, DeepEval)
• open-source AI contributions
• multi-modal models (vision, audio)

Job Summary

We are looking for an experienced AI/ML Engineer to design, develop, deploy, and maintain machine learning and AI solutions that address complex business problems. The ideal candidate should have strong hands-on experience in Python, Machine Learning, Generative AI, LLMs, and AI/ML deployment, with the ability to work across the complete AI/ML lifecycle.

Key Responsibilities

• Design, develop, and deploy scalable Machine Learning and AI models for real-world business use cases.
• Build and optimize ML pipelines covering data preparation, feature engineering, model development, evaluation, and deployment.
• Develop solutions using Generative AI, Large Language Models (LLMs), NLP, and deep learning.
• Work with LLMs, prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation (RAG) architectures.
• Integrate AI/ML models with enterprise applications and APIs.
• Fine-tune and evaluate ML/LLM models based on business requirements.
• Implement MLOps practices for model versioning, deployment, monitoring, and continuous improvement.
• Collaborate with Data Scientists, Software Engineers, Architects, Product Managers, and business stakeholders.
• Conduct model performance evaluation, optimization, and troubleshooting.
• Ensure AI solutions meet requirements around security, scalability, reliability, responsible AI, and data privacy.
• Stay current with emerging AI/ML technologies, frameworks, and industry best practices.

Required Skills

• Strong programming experience in Python.
• Strong understanding of Machine Learning algorithms, statistics, and data structures.
• Hands-on experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or equivalent.
• Experience with Generative AI and LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, or open-source models.
• Strong knowledge of Prompt Engineering, RAG, embeddings, vector databases, and AI agents.
• Experience with NLP, deep learning, or computer vision is an advantage.
• Experience developing and consuming REST APIs and microservices.
• Working knowledge of SQL and NoSQL databases.
• Experience with cloud platforms such as Azure, AWS, or GCP.
• Understanding of Docker, Kubernetes, CI/CD, and MLOps.
• Familiarity with Git and modern software development practices.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• 4 years of relevant experience in AI/ML engineering or a related field.
• Experience building and deploying production-grade AI/ML solutions.
• Enterprise application development experience.
• Experience with Azure OpenAI, AWS Bedrock, Vertex AI, or similar managed AI platforms.
• Experience with LangChain, LlamaIndex, Semantic Kernel, or comparable AI frameworks is a plus.
• Experience with AI/ML model monitoring, evaluation, and optimization.

What You Bring

• Strong problem-solving and analytical skills.
• Ability to translate business requirements into practical AI/ML solutions.
• Strong software engineering and debugging capabilities.
• Ability to work independently as well as collaboratively in a cross-functional environment.
• Good communication skills with the ability to explain complex AI concepts to technical and non-technical stakeholders.

Keywords

AI Engineer | ML Engineer | Machine Learning | Generative AI | LLM | Python | NLP | Deep Learning | RAG | Prompt Engineering | AI Agents | Azure OpenAI | AWS Bedrock | MLOps | TensorFlow | PyTorch | Scikit-learn | Vector Database | Cloud AI

Location: Gurugram

Work mode: Hybrid

Strong AI Engineer / Machine Learning Engineer profiles.

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Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

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Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

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Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

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Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

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Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

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Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

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Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

Position Overview

We are seeking a versatile Senior Full Stack & AI Agent Developer to architect, build, and

maintain end-to-end software solutions spanning web platforms, desktop applications, and

autonomous AI agents capable of interacting with and controlling these software systems.

The ideal candidate will bridge traditional engineering software with cutting-edge artificial

intelligence to automate data processing and enhance operational decision-making. While

not strictly required, a background or strong interest in the energy sector—specifically

drilling and completion operations—is highly desirable.

Key Responsibilities

• Full Stack Development: Design, develop, and deploy robust web applications and

native desktop software utilized by engineering and operational teams.

• AI Agent Engineering: Build, train, and integrate autonomous AI agents and LLM-

driven workflows capable of interpreting data, executing commands, and safely

controlling desktop and web-based software.

• Workflow Automation: Translate complex workflows into intuitive software features

and autonomous agent actions, minimizing manual data entry and operational

bottlenecks.

• Data Integration: Handle high-frequency data streams and integrate them seamlessly

into user interfaces and backend AI models.

• Architecture & Scalability: Ensure high performance, security, and scalability across

cloud infrastructure (AWS/Azure), local desktop environments, and potential edge

computing setups.

• Cross-Functional Collaboration: Work closely with domain experts and end-users to

translate field challenges into technical product requirements.

Required Qualifications & Experience

• Experience: Minimum of 5 years of professional software development experience,

with a proven track record of delivering production-ready web and desktop

applications.

• Programming Languages: Strong proficiency in Python, JavaScript/TypeScript, and at

least one compiled language (C#, C++, or Java).• Web & Desktop Frameworks: Hands-on experience with modern frontend

frameworks (React, Angular, or Vue.js), Node.js, and desktop application development

(Electron, WPF, Qt, or Tauri).

• AI & Agent Tooling: Demonstrated experience building AI agents using LLM APIs

(OpenAI, Anthropic), open-source models (Hugging Face), LangChain, LlamaIndex,

AutoGPT, or custom agent architectures.

• Automation & UI Control: Expertise in software control mechanisms using tools like

Selenium, Playwright, PyAutoGUI, Appium, or computer vision-based GUI automation to

allow AI agents to navigate software.

• Cloud, DevOps & Databases: Experience with Git, Docker, CI/CD pipelines, cloud

platforms (AWS/Azure/GCP), RESTful APIs, GraphQL, and relational/NoSQL databases.

Preferred Qualifications (Strong Plus)

• Industry Domain Expertise: Prior hands-on development experience within the oil and

gas sector, specifically focused on drilling, completions, rig operations, or subsurface

engineering software.

• Data & Protocols: Familiarity with oilfield data standards (e.g., WITSML, OPC-UA) and

time-series databases.

• Experience deploying AI models and agents in edge or low-connectivity environments

(such as offshore rigs or remote drilling sites).

• Familiarity with safety-critical software design and cybersecurity standards in

industrial control systems (ICS/SCADA).

• Degree in Computer Science, Software Engineering, Petroleum Engineering, or a related technical discipline.

Build AI where the work actually happens.

Celeco works inside real businesses to understand critical workflows, ship production AI systems and stay through adoption.

We are hiring our first Intelligence Architect (Forward-Deployed AI Engineer).

⌁

Full-time · Remote-first · Optional hybrid in Bengaluru

At least one year of professional engineering experience. Customer travel when the work requires it.

The role

An Intelligence Architect enters a customer environment with an unfinished question and leaves behind a working, measurable system.

This is a hands-on engineering role at the boundary of product, operations and customer delivery. You will learn the domain, inspect the existing systems and data, decide what should be built, and write the code that puts it into production.

What you will own

• Interview and shadow the people doing the work, then map the real workflow, including hand-offs, exceptions and workarounds.
• Understand the customer's application stack, APIs, data, identity model, security constraints and deployment environment.
• Turn business goals into a technical scope, system design, delivery plan and measures of success.
• Build across the stack: AI workflows, data pipelines, integrations, backend services and the interfaces people use.
• Choose the simplest reliable approach. The answer may combine agents, retrieval, rules and conventional software.
• Create evals from representative cases, define quality and failure metrics, inspect traces, red-team the system and set launch thresholds.
• Make practical trade-offs across accuracy, latency, cost, privacy, security and speed.
• Take prototypes into production with tests, monitoring, access controls, documentation and a plan for failure and recovery.
• Work beside customer teams during rollout and improve the system until it becomes part of the workflow.
• Turn what works into reusable components, evaluation sets and playbooks for future Celeco deployments.

You will probably thrive here if

• You have at least one year of professional software or product engineering experience.
• You have shipped a real system used by other people and can explain what you owned, what broke and what you changed.
• You are strong in Python or TypeScript and comfortable moving across unfamiliar codebases, APIs, databases and cloud services.
• You have built with language or multimodal models and understand prompting, structured outputs, retrieval, tool use and model failure modes.
• You use Cursor, Codex, Claude Code or similar tools as part of your engineering workflow, while still reviewing, testing and understanding the code you ship.
• You can create an evaluation set, choose useful quality metrics and improve a system through error analysis instead of prompt guesswork.
• You can speak with an operator, an engineering team and a senior leader without losing the thread of the problem.
• You work well with incomplete requirements, write clearly and surface risks early.
• You care about whether people use what you build and whether it changes a business outcome.
• You can commit full-time and travel to customer sites when discovery or rollout is better done in person.

Experience with cloud deployment, containers, CI/CD, observability, enterprise integrations, authentication or security is useful. We do not expect one person to arrive knowing every framework, cloud or industry.

What you will get

• Direct ownership of live customer problems from discovery through production.
• Close collaboration with Celeco's founders and customer leadership teams.
• Exposure to different industries, operating models and technical environments.
• The freedom to choose the technical approach and the responsibility to prove that it works.
• A role in defining Celeco's engineering methods, reusable systems and technical culture while the company is early.
• A remote-first setup, with the option to work together in Bengaluru.

If the work sounds like you but your background is unconventional, apply. We care more about what you have built, how you think and how quickly you learn than pedigree.

Job Title: Senior AI Engineer

Experience: 5–10 years in software engineering, with 3+ years in AI/ML engineering preferred

Employment Type: Full-time

Department: Artificial Intelligence / Engineering

Location: Bangalore

We are looking for a highly skilled Senior AI Engineer to design, develop, deploy, and scale intelligent AI-powered applications and solutions. The ideal candidate will have strong expertise in machine learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and production-grade software engineering.

The candidate will work closely with product managers, data scientists, software engineers, and business stakeholders to translate complex business requirements into scalable, reliable, secure, and cost-effective AI solutions.

Key Responsibilities

1. AI Solution Design and Development

• Design, develop, and deploy end-to-end AI and machine learning solutions.
• Build intelligent applications using LLMs, Generative AI, NLP, and deep learning techniques.
• Develop scalable AI architectures and reusable components for enterprise applications.
• Evaluate AI models and select appropriate approaches based on accuracy, latency, cost, and business requirements.

2. Generative AI and LLM Engineering

• Develop LLM-powered applications using models from providers such as OpenAI, Anthropic, Google, or open-source ecosystems.
• Design and implement RAG pipelines, document processing workflows, semantic search, and knowledge retrieval systems.
• Build AI agents and multi-step workflows using appropriate orchestration frameworks.
• Implement prompt engineering, structured outputs, tool calling, and context management.
• Optimize model performance through evaluation, fine-tuning where appropriate, and inference optimization.

3. Machine Learning and Data Engineering

• Develop and operationalize machine learning models for classification, prediction, recommendation, and other business use cases.
• Work with structured, unstructured, and multimodal data.
• Build data preprocessing, feature engineering, model training, and validation pipelines.
• Apply appropriate model evaluation techniques and statistical methods.

4. Production Deployment and MLOps / LLMOps

• Deploy AI solutions using cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Build automated CI/CD pipelines for AI applications and model deployment.
• Implement model versioning, experiment tracking, monitoring, and performance evaluation.
• Optimize inference latency, throughput, infrastructure utilization, and operating costs.
• Establish observability for AI applications, including token usage, retrieval quality, latency, failures, and model behavior.

5. Software Engineering and System Architecture

• Develop production-quality applications and APIs using Python and modern backend frameworks.
• Design RESTful APIs, asynchronous processing systems, and microservices where appropriate.
• Integrate AI services with enterprise applications, databases, and third-party systems.
• Use relational databases, vector databases, caching, and distributed systems effectively.
• Apply software engineering best practices, including testing, code reviews, documentation, and design patterns.

6. Security, Quality, and Responsible AI

• Implement safeguards against prompt injection, data leakage, insecure tool execution, and unauthorized access.
• Apply appropriate privacy, security, and compliance controls to AI systems.
• Evaluate hallucinations, bias, reliability, and failure modes.
• Design human-in-the-loop workflows and fallback mechanisms for critical use cases.

7. Technical Leadership and Collaboration

• Lead technical design discussions and mentor junior AI engineers.
• Define engineering standards, coding practices, and AI development guidelines.
• Collaborate with cross-functional teams to deliver measurable business outcomes.
• Conduct code reviews, evaluate emerging technologies, and recommend practical innovations.
• Communicate technical trade-offs and solution architecture to both technical and non-technical stakeholders.

Required Technical Skills

Programming and Backend Development

• Strong proficiency in Python.
• Experience with FastAPI, Flask, or Django.
• Knowledge of REST APIs, asynchronous programming, data structures, algorithms, and software design principles.
• Experience with Git, automated testing, and CI/CD.

AI and Machine Learning

• Strong understanding of supervised and unsupervised learning, model evaluation, and statistical concepts.
• Hands-on experience with PyTorch, TensorFlow, or scikit-learn.
• Knowledge of NLP, embeddings, transformers, and deep learning fundamentals.

Generative AI and LLMs

• Hands-on experience building production applications using LLM APIs or open-source models.
• Strong understanding of RAG architecture, embeddings, chunking, reranking, and retrieval evaluation.
• Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or pgvector.
• Familiarity with orchestration frameworks such as LangChain, LangChain, or comparable tools.
• Experience with tool calling, structured generation, agent workflows, and LLM evaluation.

Cloud and Infrastructure

• Experience with AWS, Azure, or Google Cloud.
• Working knowledge of Docker and Kubernetes.
• Familiarity with model serving, scalable APIs, monitoring, and production deployments.
• Experience with MLOps or LLMOps practices.

Databases and Data Processing

• Strong SQL skills and experience with relational databases.
• Familiarity with NoSQL databases, data pipelines, and distributed processing.
• Experience working with unstructured data, document ingestion, and retrieval systems.

Preferred Qualifications

• Experience with fine-tuning, quantization, or serving open-source LLMs.
• Experience with multimodal AI, including text, image, audio, or video.
• Knowledge of agent evaluation, workflow orchestration, and multi-agent systems.
• Experience with AI security, guardrails, governance, and responsible AI.
• Experience optimizing AI workloads for latency, scalability, and cost.
• Contributions to open-source AI projects, research, or technical publications.

Education

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• Equivalent practical experience and demonstrated engineering expertise may be considered.

Key Success Metrics

• Quality, accuracy, and reliability of deployed AI solutions.
• Production adoption and measurable business impact.
• Reduction in inference costs and response latency.
• RAG retrieval quality and reduction in unsupported or incorrect responses.
• System availability, scalability, and operational stability.
• Delivery timelines, code quality, and technical mentoring effectiveness.

Hiring for AI Engineer

Exp: 5 - 8 yrs

Edu : BE/B.Tech/MCA

Work Location : Pune

Skill Set:

Total experience ranging from 5 - 8 years in software engineering/AI roles

Min 5 years strong programming experience in Python or Typescript is a MUST

Min 2.5 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks

Experience with cloud platforms (AWS/Azure/GCP)