Generative AI Engineer

Tata Consultancy Services; Chennai District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Tata Consultancy Services in Chennai is seeking an experienced backend engineer to build and productionize cloud native services and AI/LLM pipelines. You will design and develop Python-based APIs and microservices (FastAPI, async patterns) and agentic AI workflows using LangChain/LangGraph.

You will implement LLM capabilities including embeddings, RAG, vector search, and model versioning. Responsibilities include packaging, serving, and monitoring models for real-time and batch inference,

Qualifications

• Experience building cloud-native backend services.
• Strong Python API development skills (FastAPI).
• Hands-on with AI/LLM workflows and LangChain-based pipelines.

Responsibilities

• Build and productionize cloud-native backend services and AI/LLM inference pipelines.
• Design and develop Python-based APIs and microservices (FastAPI, async patterns).
• Implement and optimize LLM capabilities including embeddings, RAG, vector search, prompt/context engineering, and model versioning.
• Package, serve, and monitor models for real-time and batch inference, ensuring operational readiness and performance.
• Build event driven, resilient integrations and containerized services, with hands-on Kubernetes debugging and Helm-based deployments.
• Apply strong systems design principles (concurrency, caching, reliability, rate limiting) and robust data engineering practices.
• Cloud exposure preferred (Azure/AKS, managed services), with bonus experience in performance tuning, frontend collaboration, and model governance/monitoring.

Skills

Python

FastAPI

Async patterns

LangChain

LangGraph

Kubernetes

Helm

Embeddings/RAG

Vector search

Prompt engineering

Model versioning

Concurrency

Caching

Rate limiting

Data engineering

Azure AKS

Tools

Kubernetes

Helm

Azure AKS

Job description

• Build and Productionize cloud native backend services and AI/LLM inference pipelines.
• Design and develop Python-based APIs and microservices (FastAPI, async patterns) and agentic AI workflows using LangChain/LangGraph.
• Implement and optimize LLM capabilities including embeddings, RAG, vector search, prompt/context engineering, and model versioning.
• Package, serve, and monitor models for real-time and batch inference, ensuring operational readiness and performance.
• Build event driven, resilient integrations and containerized services, with hands-on Kubernetes debugging and Helm-based deployments.
• Apply strong systems design principles (concurrency, caching, reliability, rate limiting) and robust data engineering practices.
• Cloud exposure preferred (Azure/AKS, managed services), with bonus experience in performance tuning, frontend collaboration, and model governance/monitoring.