Tata Consultancy Services
AI/ML Engineer (Generative AI & Agentic AI)
Skills
4 days ago
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Important information
Contract type:
Permanent
Location:
Bengaluru, Karnataka, India
Work mode:
Onsite
Published on:
18 October 2026
What they need
We are looking for an experienced AI/ML Engineer with expertise in Generative AI, Agentic AI, Large Language Models (LLMs), and AWS AI Services. The ideal candidate will design, develop, and deploy enterprise-grade AI solutions using Amazon Bedrock, SageMaker, Lambda, LangChain, and LangGraph while building intelligent applications powered by foundation models such as Claude.
The candidate should possess strong hands-on experience in prompt engineering, Retrieval-Augmented Generation (RAG), multi-agent orchestration, model evaluation, and cloud-native AI architectures.
Key Responsibilities
- Design and deploy scalable AI/ML and Generative AI solutions on AWS.
- Develop Agentic AI applications using LangChain and LangGraph.
- Build multi-agent workflows and orchestration frameworks.
- Develop LLM-powered applications using Claude and other foundation models.
- Implement prompt engineering, RAG, embeddings, and model evaluation frameworks.
- Integrate AI services with enterprise applications and APIs.
- Build secure, scalable, and high-performance AI systems.
- Collaborate with business and engineering teams to define AI use cases and architectures.
- Provide technical guidance and mentorship on AI/ML best practices.
- Drive AI innovation through adoption of emerging tools and frameworks.
Required Skills
5-10 years of AI/ML engineering experience.
Strong expertise in:
Amazon Bedrock
SageMaker
AWS Lambda
Python
Hands-on experience with:
LangChain
LangGraph
Agentic AI Frameworks
Strong knowledge of:
LLMs
Prompt Engineering
RAG
Model Evaluation
Experience integrating AI solutions with enterprise systems and APIs.
Understanding of AI security, governance, and performance optimization.
Experience working in Agile environments.
Preferred Skills
- Vector Databases (Pinecone, FAISS, OpenSearch).
- Multi-Agent Architectures.
- MLOps & LLMOps.
- Responsible AI Frameworks.
- AI Governance.
- AWS AI/ML Certifications.
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