AI Engineer (AI Agents, Gen AI, Python)

Tata Consultancy Services Limited; Bengaluru; On-site; INR 900,000 - 1,300,000; Full time

Job summary

Tata Consultancy Services Limited is seeking a highly skilled AI Engineer to design and deploy AI-powered applications using Generative AI, LLMs, and Python. The role emphasizes building AI Agents for workflow automation and implementing RAG architectures.

You will work with vector databases, REST APIs, and cloud-based AI solutions, collaborating with cross-functional teams to deliver scalable, AI-driven business solutions and continuous model optimization.

Qualifications

• Strong proficiency in Python and hands-on experience with Generative AI, LLMs, and AI Agent development.
• Experience with LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar frameworks.
• Experience implementing RAG systems and knowledge of prompt engineering.
• Familiarity with vector databases (Pinecone, FAISS, ChromaDB, Weaviate).
• Experience with REST APIs, AI model integrations, and ML/NLP concepts.
• Knowledge of Git, CI/CD pipelines, and Agile methodologies.
• Exposure to cloud AI solutions and MLOps practices.

Responsibilities

• Design, develop, and deploy AI-powered applications using Generative AI technologies.
• Build and implement AI Agents for intelligent workflow automation and decision-making.
• Develop scalable solutions using Python and modern AI frameworks.
• Design and implement RAG architectures for retrieval-augmented generation.
• Integrate LLMs (OpenAI, Azure OpenAI, Claude, Gemini, etc.) and optimize prompts.
• Create APIs and microservices for AI applications.
• Work with vector databases for semantic search and knowledge retrieval.
• Collaborate with cross-functional teams to deliver AI-driven business solutions.
• Monitor, evaluate, and optimize model performance, accuracy, and scalability.

Skills

Python programming

Generative AI

LLMs

AI Agents development

Prompt engineering

Vector databases

REST APIs

CI/CD

MLOps

Tools

LangChain

LangGraph

CrewAI

AutoGen

Semantic Kernel

Kubernetes

Docker

TensorFlow

PyTorch

Weaviate

Pinecone

FAISS

ChromaDB

Job description

Job Description We are seeking a highly skilled AI Engineer with strong expertise in AI Agents, Generative AI, LLMs, and Python to design and build next-generation AI-powered applications. The ideal candidate should have hands-on experience with agentic AI frameworks, prompt engineering, RAG architectures, vector databases, and cloud-based AI solutions.

Key Responsibilities

Design, develop, and deploy AI-powered applications using Generative AI technologies. Build and implement AI Agents for intelligent workflow automation and decision-making. Develop scalable solutions using Python and modern AI frameworks. Design and implement RAG (Retrieval-Augmented Generation) architectures. Integrate Large Language Models (LLMs) such as OpenAI, Azure OpenAI, Claude, Gemini, or open-source models. Create and optimize prompts for improved AI model performance. Develop APIs and microservices for AI applications. Work with vector databases for semantic search and knowledge retrieval. Collaborate with cross-functional teams to deliver AI-driven business solutions. Monitor, evaluate, and optimize model performance, accuracy, and scalability.

Required Skills

Strong proficiency in Python programming. Experience in Generative AI, LLMs, and AI Agent development. Hands-on experience with LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar frameworks. Experience in implementing RAG systems. Knowledge of Prompt Engineering and AI evaluation techniques. Familiarity with Vector Databases such as Pinecone, FAISS, ChromaDB, or Weaviate. Experience with REST APIs and AI model integrations. Understanding of Machine Learning and NLP concepts. Experience with Git, CI/CD pipelines, and Agile methodologies. Good to Have Experience with Azure OpenAI, AWS Bedrock, or Google Vertex AI. Knowledge of Kubernetes and Docker. Experience with MLOps practices and model deployment. Exposure to AI monitoring and observability tools. Knowledge of ML frameworks such as TensorFlow or PyTorch. Experience in cloud-native application development.