AI Engineer at Neosapien · Bengaluru (Bangalore) · 3 - 7 years · ₹15L - ₹40L / yr (ESOP available) · Posted 2 Sep 2026
Python
Machine Learning (ML)The Role
You own AI systems end to end. From the speech-to-text models that turn audio into text, to the diarization that separates and identifies speakers, to the agentic layer that turns conversation into memory and action, to the observability and evaluation that keep all of it honest in production. This is a wide role by design. You will own model selection, serving, and production reliability. If you want to tune one model and ignore the system around it, this is not the role.
What You Will Own
• Speech-to-text. Evaluate, integrate, and optimize STT models across cloud and self-hosted. Drive accuracy and cost trade-offs with ground-truth metrics.
• Speaker diarization and identification. Push accuracy on hard, real-world, multi-speaker audio.
• Agentic AI. Build the memory and retrieval pipeline, LLM orchestration, and the agent workflows that sit on top of captured conversation.
• Model serving and infrastructure. Stand up and optimize self-hosted serving (vLLM, Triton class). Own latency, throughput, and cost per user.
Observability
An always-on wearable means models run in production every second, on messy real-world audio. You own the visibility into that.
• Instrument the full audio-to-memory pipeline: STT, diarization, retrieval, and LLM calls.
• Define and track model-quality SLOs in production: transcription drift, diarization error over time, retrieval relevance, latency, throughput, and cost per user.
• Build dashboards and alerting so model degradation is caught before users feel it.
• Trace failures across a distributed, always-on system using metrics, logs, and traces.
• Close the loop. Production signals feed back into evaluation and model selection.
Evaluation
We do not ship what we cannot measure. You own the systems that prove a model is actually better, not just newer.
• Build and own ground-truth evaluation harnesses for every model in the stack.
• Measure with real metrics: WER for transcription, DER for diarization, Recall and F1 for retrieval and speaker identification.
• Build and maintain labeled benchmark datasets that reflect real, messy, multi-speaker audio.
• Run regression and A/B evaluations on every model swap, prompt change, or pipeline update. Nothing ships on a vibe.
• Reject anecdotal proxies, single confidence scores, and cherry-picked examples as evidence of quality.
What We Are Looking For
• 3 to 5 years as an AI/ML engineer with production systems behind you. Engineering and production experience is non-negotiable.
• Depth across the modern AI stack: LLMs, speech models, vector retrieval, model serving.
• Strong software engineering. You write code that ships and survives contact with real users.
• Fluency in Python and the production ML ecosystem.
• Comfort with cloud infrastructure (GCP a plus) and containerized deployment on Kubernetes.
• A working command of observability and evaluation. You measure first and trust metrics over intuition.
• First-principles reasoning and metric discipline.
Nice to Have
• Research background or publications. A strong signal, not a substitute for production work.
• Audio and speech ML experience (STT, diarization, voice).
• Experience self-hosting and optimizing open models.
• Experience with LLM gateway and agent orchestration patterns.
• Experience building eval harnesses or production model-monitoring systems.
Requirements
Agentic work is must. Audio is good to have
. Self hosting models is a must
Experience with LLM gateway and agent orchestration is a must have
Shubham Vishwakarma
Full Stack Developer - Averlon
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- Additional Job Description
Additional Job Description
Required Skills and Experience:
- Strong proficiency in Python and experience with ML/AI libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face ecosystem).
- Hands-on experience with LLMs, RAG, vector databases, and retrieval pipelines.
- Practical experience deploying agentic workflows and building multi-step, tool-enabled agents.
- Experience using Garak (or similar LLM red-teaming/vulnerability scanners) to identify model weaknesses and harden deployments.
- Demonstrated experience implementing content filtering / moderation systems.
- Solid skills working with structured and unstructured data and advanced feature engineering.
- Familiarity with cloud GenAI platforms and services (Azure AI Services preferred; AWS/GCP acceptable).
- Experience building APIs/microservices; containerization (Docker), orchestration (Kubernetes).
- Strong understanding of model evaluation, performance profiling, inference cost optimization, and observability.
- Good knowledge of security, data governance, and privacy best practices for AI systems.
PythonAI 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)
PythonSenior AI Engineer
Code Generation, Agent Architecture & LLM Systems
📍 Mumbai (On-site) | Full-time | 5+ 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.
We are hiring a Senior AI Engineer for a dedicated client engagement focused on building an AI-powered application builder platform - a product where users describe software in plain English and the system generates, previews, and iteratively refines working code.
The mandatory requirement for this role is hands-on production experience shipping LLM-powered systems with agent architectures, with experience in code generation or developer tooling contexts a strong advantage.
The role is product-focused and deeply hands-on. You will own everything between the user's prompt and correct code landing in the project: the agentic loop, code generation pipeline, context management, evaluation suite, and model cost strategy.
You will work alongside the Senior MLOps Engineer who operationalises the infrastructure around your system, and collaborate closely with backend, frontend, and DevOps engineers.
Responsibilities:
Agent Architecture
Design and own the agentic loop for the platform - request interpretation, planning, tool-calling sequence (read file, edit file, run build, search code, install package), and stop conditions.
Make and revisit architectural decisions on single-agent vs. multi-agent designs, including planner/executor splits and dedicated build-repair sub-agents.
Code Generation Pipeline
Own the end-to-end generation flow: task classification, context gathering, planning, targeted edits, verification, and commit.
Implement diff/search-replace-based file editing with fuzzy matching and fallback strategies.
Enforce scope discipline so the agent makes minimal diffs and does not modify code it was not asked to touch.
Self-Repair Loop
Build and tune the automated repair loop that pipes compiler, lint, build, and runtime errors back to the model with retry budgets and model escalation.
This loop is the primary quality lever - the difference between 60-70% and 90%+ build success rates.
Context Management
Build file-relevance retrieval so the agent sees the right files, not the whole codebase: dependency graphs, AST/tree-sitter-based chunking, embeddings, recency signals, and hybrid retrieval.
Implement conversation summarisation and memory for long sessions, and address long-project degradation through codebase summaries and periodic consistency passes.
Own token budgeting and prompt caching strategy.
Prompt Engineering as a Discipline
Own the system prompt and per-task prompt variants (new feature, bug fix, styling change).
Maintain few-shot examples and enforce coding conventions, stack rules, and prohibited behaviours such as no hardcoded secrets and no whole-file rewrites.
Version prompts like code with changelogs and rollback capability.
Evaluation and Quality Measurement
Design and own the evaluation suite: representative test prompts run on every prompt and model change, scored on build success rate, instruction adherence, and output quality including LLM-as-judge and visual/screenshot checks where relevant.
Define regression gates that block quality-degrading changes from shipping.
Treat evals the way engineers treat automated testing: versioned, automated, and tracked over time.
This responsibility is non-negotiable at this level.
Model Strategy and Cost
Design model routing - cheap and fast models for classification and small edits, frontier models for complex generation.
Drive cost optimisation through prompt caching, diff-based edits over full-file rewrites, and tighter context selection.
Track cost per agent run and tokens per task; evaluate new model releases against the eval suite and lead migrations when results justify it.
Safety and Reliability of Agent Behaviour
Defend against prompt injection from user content and fetched web content.
Ensure secrets never appear in generated client code.
Define what the agent's tools may and may not do in collaboration with the platform team.
Contribute to output moderation and abuse-pattern awareness.
Mentorship and Engineering Standards
Run code reviews, define engineering conventions for AI work, and raise the engineering bar across the AI team.
Work closely with the Senior MLOps Engineer on handoff of eval design, prompt configurations, and model routing logic.
Requirements:
Hands-on Production Ownership of LLM-Powered Systems with Agent Architectures (Mandatory)
Must have personally shipped and operated at least one complex production AI system - agentic, multi-step, or code generation - with end-to-end ownership of architecture, evaluation, and cost.
POCs, internal demos, and tutorial-grade work do not qualify.
5+ Years of Professional Software or AI Engineering Experience
With at least 3 years focused on LLM applications, AI engineering, or production AI systems.
Candidates with strong backend backgrounds and a clear, substantive pivot into LLM systems qualify.
Strong Python Proficiency and Service Development
Production-grade Python with FastAPI or equivalent: type hints, async patterns, streaming responses, testing, and packaging.
Not notebook-only.
Depth Across LLM APIs and Agent Systems
Production experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or open-weight models (vLLM, Ollama, Together).
Production experience with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.
Hands-on with tool calling, structured outputs, and multi-step reasoning.
Demonstrated, Systematic Evaluation Practice - Non-Negotiable
Must have built evaluation harnesses that gate production releases, not ad-hoc testing.
Hands-on with at least one of LangSmith, Langfuse, Promptfoo, Ragas, or DeepEval.
Candidates with no systematic answer to evaluation should not be considered at senior level regardless of other strengths.
Cost Discipline for Production AI
Track record of measurable cost optimisation on production AI features.
Able to speak in specifics: cost per request, savings achieved through caching or model routing, context reduction decisions.
AWS Working Knowledge
Hands-on with EC2, S3, IAM, and Docker.
Comfort with CI/CD workflows and deploying AI services.
Awareness of LLM Security Failure Modes
Familiar with prompt injection patterns, understands that system prompt rules alone are insufficient, and has experience with output validation and content safety in production.
Nice to Have
- Experience with AST/tree-sitter tooling, diff-based editing systems, or compiler-adjacent work
- MCP server authoring
- Open-source AI contributions
- Published technical writing on LLM systems
- Multi-modal model experience
- Fine-tuning exposure (LoRA, QLoRA, PEFT)
Hiring for AI Engineer
Exp: 5 - 10 yrs
Edu : BE/B.Tech/MCA
Work Location : Pune / Mumbai
Skill Set:
Total experience ranging from 5–10 years in software engineering/AI roles
Min 5 years strong programming experience in Python is a MUST
Min 3.5 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks
2+ years shipping LLM systems in production
Experience with cloud platforms (AWS/Azure/GCP)
Data SciencePythonSupport with design and build to prove out agentic AI solution flow by working with other data
scientists and engineers to build, train Large Language Model (LLM) architectures, RAG
systems, and autonomous agentic workflows
Key qualifications:
>> AI solution design & Development: Design Agentic AI solutions using RAG (Retrieval-
Augmented Generation) and orchestration frameworks like LangGraph or LangChain.
>> Model Fine-Tuning: Solid understanding and experience with Pre-train, fine-tune, and
optimize open-source like BERT, LLama, and other proprietary foundation models for domain-
specific tasks
>> Solid Stats and ML foundations and (vibe) coding skills with Python, PySpark
>> Implement validation frameworks and tracing practices (using tools like Arize) to monitor
agent behavior, guard against model drift, and ensure compliance
>> Collaborate with Engineering to deploy models securely on cloud and on-prem ecosystems
Machine Learning (ML)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
Amazon Web Services (AWS)
Docker
FlaskJob Title: Senior AI/ML Engineer
Company: Timble Technologies Pvt. Ltd
Location: Gurugram (Hybrid)
Experience: 2 TO 5 Years
About Us
Timble Glance is a high-growth AI RegTech and B2B SaaS company catering to top-tier BFSI and enterprise clients. We build cutting-edge systems powering 30+ high-scale APIs for digital identity verification, fraud detection, document intelligence, and compliance automation.
Role Overview
We are looking for a hands-on Senior AI/ML Engineer to design, develop, and productionize high-throughput AI/ML and Generative AI systems. You will own the full lifecycle—from problem formulation and data pipelines to deep learning architectures, RAG systems, LLMOps, and model governance—delivering sub-second latency and high reliability across our enterprise products.
Key Responsibilities
· Model Architecture & Deployment: Design, train, and deploy production-scale ML/Deep Learning and GenAI systems (computer vision, document intelligence, OCR, NLP, fraud risk classification, and LLM applications).
· GenAI & LLM Solutions: Develop robust LLM workflows including prompt engineering, fine-tuning, RAG pipelines, semantic search, vector indexing (Pinecone/Milvus/Chroma), and safety guardrails.
· Pipelines & Engineering: Build performant feature extraction and data pipelines; write modular, vectorized, production-grade Python (NumPy, Pandas) and advanced SQL.
· MLOps & Monitoring: Establish end-to-end MLOps/LLMOps standards—model registries, CI/CD, experiment tracking, drift detection, A/B testing, latency optimization, and cost governance.
· Responsible AI & Security: Ensure model decisions comply with enterprise data security, privacy standards, and auditability required by the BFSI sector.
· Collaboration & Ownership: Translate complex business requirements into technical roadmaps, conduct rigorous code reviews, and mentor junior engineers.
Required Qualifications & Skills
· Education: B.Tech / M.Tech in Computer Science, AI/ML, Mathematics, or a related field—Tier-1 institutes (IIT, IIIT, NIT) strongly preferred.
· Experience: 2+ years of hands-on experience developing, deploying, and maintaining ML/Deep Learning or GenAI models in production environments.
· GenAI & NLP Stack: Hands-on experience with LLMs, embeddings, RAG architectures, and frameworks such as LangChain, LlamaIndex, or Hugging Face.
· Deep Learning Frameworks: Strong proficiency in PyTorch or TensorFlow, with deep knowledge of transformer architectures and modern NLP/CV models.
· Software & Data Engineering: Expert-level Python skills (pytest, Git, OOP, asynchronous programming), solid SQL proficiency, and familiarity with data workflows.
· Deployment & Cloud: Practical exposure to cloud platforms (AWS/GCP), containerization (Docker), API frameworks (FastAPI/Flask), and basic orchestration (Kubernetes).
Preferred Qualifications
· Prior domain experience in Fintech, RegTech, Identity Verification (KYC/AML), Fraud Intelligence, or B2B SaaS.
· Experience optimizing models for low latency and inference cost (e.g., ONNX, TensorRT, model quantization).
· Familiarity with workflow orchestrators such as Airflow, Prefect, or Kubeflow.
About LeadSquared
LeadSquared is a leading sales execution and marketing automation platform trusted by 2,000+ businesses globally, including healthcare, education, financial services, and real estate. Headquartered in Bengaluru with offices across the US, UK, UAE, and Southeast Asia, we empower sales teams to close faster, smarter, and at scale.
Our AI team is at the forefront of integrating cutting-edge large language model capabilities into enterprise workflows — building intelligent agents, copilots, and automation systems that redefine how businesses operate.
Role Overview
We are looking for a Senior AI Engineer with hands-on experience building LLM-powered agents and agentic AI systems. You will design, develop, and deploy autonomous AI pipelines that solve complex, multi-step business problems — from lead qualification and follow-up automation to intelligent CRM workflows and beyond.
This role is ideal for someone who is deeply excited about the frontier of AI, can move fast, and wants their work to directly impact millions of sales professionals worldwide.
Key Responsibilities
•
Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.
•
Develop and maintain Retrieval-Augmented Generation (RAG) pipelines with vector databases (Pinecone, Weaviate, Chroma, pgvector) for domain-specific knowledge grounding.
•
Build and integrate tool-use and function-calling capabilities into AI agents, enabling dynamic interaction with internal APIs, databases, and third-party services.
•
Implement prompt engineering strategies including chain-of-thought, few-shot prompting, and structured output parsing to ensure reliable agent behavior.
•
Design evaluation frameworks and observability pipelines (LangSmith, Helicone, custom metrics) to monitor agent performance, accuracy, and cost.
•
Collaborate with product, sales, and domain teams to translate business requirements into AI-driven solutions and features.
•
Optimize LLM inference for latency and cost using techniques like caching, model distillation, quantization, and batching.
•
Stay current with the rapidly evolving LLM ecosystem and proactively propose improvements and new approaches.
•
Contribute to internal best practices, documentation, and knowledge-sharing across the engineering org.
Required Qualifications
Experience
•
2–4 years of professional software engineering experience, with at least 1–2 years focused on LLM/AI systems.
•
Proven experience shipping LLM-based products or agentic AI systems into production environments.
Technical Skills
•
Strong proficiency in Python and familiarity with async programming patterns for AI pipelines.
•
Hands-on experience with LLM APIs: OpenAI (GPT-4o), Anthropic (Claude), Google (Gemini), or open-source models (Llama, Mistral).
•
Experience with agentic frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or similar.
•
Solid understanding of RAG architectures, embedding models, and semantic search.
•
Experience with vector databases and similarity search infrastructure.
•
Knowledge of REST APIs, microservices architecture, and containerization (Docker/Kubernetes).
Problem-Solving & Mindset
•
Strong ability to decompose ambiguous, open-ended problems into structured AI system designs.
•
Experience with prompt debugging, LLM evaluation, and iterative refinement workflows.
•
Ability to balance research exploration with engineering pragmatism to ship reliable systems.
Preferred Qualifications
•
Experience with multi-agent orchestration and agent memory systems (short-term and long-term).
•
Familiarity with fine-tuning or RLHF workflows for domain adaptation.
•
Background in NLP, information retrieval, or conversational AI.
•
Prior experience in B2B SaaS or CRM domain is a plus.
•
Contributions to open-source AI/ML projects or published research/blogs.
•
Experience with cloud platforms: AWS, GCP, or Azure — particularly AI/ML services
Strong AI Engineer / Machine Learning Engineer profiles.
2
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.
3
Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.
4
Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.
5
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.
6
Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.
7
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.
8
Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.
9
Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.
10
Mandatory (Age) - Candidate's Age should be below 28 Years
Design and develop Agentic AI systems using LLMs, tools, memory,
workflows, and MCP.
Build production-grade RAG pipelines, including ingestion, chunking,
embeddings, retrieval, reranking, and evaluation.
Implement context engineering strategies for improving LLM accuracy,
relevance, and reliability.
Develop and integrate MCP-based tools and services for AI agents.
Work with LLMs, SLMs, quantized models, and model optimization
techniques for efficient inference.
Develop scalable backend services and APIs for AI applications.
Design databases and data models supporting AI/agentic applications.
Implement AI observability covering latency, token usage, cost, failures,
quality, and agent/tool execution.
Apply AI governance and responsible AI practices, including security,
access control, data privacy, and auditability.
Optimize AI systems for latency, scalability, cost, and reliability.
Collaborate with engineering and product teams to take AI solutions from
POC to production.
Strong hands-on experience with GenAI, LLMs, and Agentic AI.
Experience building RAG applications.
Strong understanding of Context Engineering and prompt/context
optimization.
Role Overview
We are looking for a hands-on AI/ML Engineer to design, develop, and deploy
production-ready GenAI and Agentic AI applications. The role involves building
intelligent agents, RAG pipelines, AI APIs, backend services, and scalable AI
infrastructure with a strong focus on context engineering, observability,
governance, and model optimisation.
Key Responsibilities
Required Skills
Practical experience with MCP (Model Context Protocol).
Experience with frameworks such as LangChain, LangGraph,
LlamaIndex, or equivalent.
Knowledge of LLM/SLM deployment and quantization techniques.
Strong Python backend development experience.
Experience developing REST APIs using FastAPI/Flask or equivalent.
Strong understanding of SQL/NoSQL databases and database design.
Experience with vector databases such as Qdrant, Pinecone, Weaviate,
ChromaDB, or FAISS.
Understanding of AI observability, evaluation, monitoring, and
governance.
Experience with cloud platforms and production deployment is preferred.
Strong understanding of software engineering principles, Git, testing, and
CI/CD.
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon