AI Engineering lead
- Source: Naukri
- Tata Consultancy Services
- On-site
- Bengaluru
- Full-Time
- Senior
Job Description
Role & responsibilities
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Architect and maintain agentic pipelines (LangGraph, CrewAI, AutoGen).
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Implement RAG, prompt engineering, context/memory/state management.
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Develop, fine-tune, and evaluate LLM models (Bedrock, OpenAI, Anthropic).
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Prototype classical ML models (forecasting, clustering, scoring) when required for hybrid AI systems.
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Deploy via SageMaker / ECS / Lambda with observability hooks
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Implement guardrails, evaluation metrics, and cost/performance optimization.
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Advise BA team on model selection, explainability, and feasibility.
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Python backend devloper having understanding of design patterns .
Preferred candidate profile
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LangGraph / CrewAI / AutoGen / LangChain / LlamaIndex
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Python (async), FastAPI, MLflow, PyTorch / Scikit-learn
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LLM fine-tuning (LoRA, PEFT, embeddings, prompt tuning)
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Vector DBs: FAISS, Pinecone, Chroma
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AWS: Bedrock, SageMaker, Step Functions, ECR
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Statistical ML, feature engineering, XAI (SHAP/LIME)
Skills
- LangGraph
- crewai
- bedrock
- amazon-ecs
- guardrails
- Python
- LangChain
- LlamaIndex
- FastAPI
- ml-flow
- mlflow
- PyTorch
- Scikit-learn
- FAISS
- chroma
- amazon-web-services
- amazon-sagemaker
- aws-step-functions
- amazon-ecr
- xai
