- We are looking for experienced AI ML Engineers who can build and productionize enterprise grade Machine Learning Generative AI and Agentic AI solutions
- The ideal candidate will combine solid Python and Machine Learning fundamentals with hands on experience in AWS AI ML services Retrieval Augmented Generation RAG LLM integration and agentic workflows
- You should be comfortable taking solutions from experimentation and model development through deployment evaluation monitoring and production operations
- This is a hands on engineering role for professionals who have built real ML GenAI solutions rather than only experimented with LLM APIs
Key Responsibilities:
- Design and deploy end to end ML and Generative AI pipelines on AWS using Amazon SageMaker and Amazon Bedrock
- Build intelligent LLM powered agents and Agentic AI workflows using LangGraph and or LangChain
- Architect production ready RAG solutions covering document ingestion chunking embeddings metadata vector indexing retrieval and response generation
- Develop and productionize ML models for use cases such as forecasting recommendations prediction and customer analytics
- Integrate foundation models and LLM APIs including Amazon Bedrock and OpenAI into scalable enterprise applications
- Engineer robust prompting context management and tool calling orchestration workflows for LLM applications
- Implement LLM evaluation grounding hallucination mitigation and responsible AI guardrails
- Build semantic and hybrid retrieval solutions with vector search technologies such as FAISS Pinecone and OpenSearch
- Apply MLOps LLMOps principles for model and application deployment versioning evaluation monitoring and lifecycle management
- Develop scalable Python based APIs and services to expose AI ML capabilities to downstream applications
- Collaborate with data engineers architects product teams and cloud platform engineers to move AI solutions from prototype to production
- The emphasis on RAG construction evaluation production agents vector indexing and LangGraph LangChain closely reflects recent Infosys internal AI engineering requiremen
Technical Requirements:
- Must Have Skills
- Candidates should have strong hands on experience in most of the following
- Programming ML
- Strong Python
- Scikit learn
- TensorFlow and or PyTorch
- Machine Learning model development and production deployment
- ML pipelines and MLOps concepts
- Generative AI Agentic AI
- Generative AI LLM application development
- LangGraph and or LangChain
- Agentic AI AI Agents
- Retrieval Augmented Generation RAG
- Prompt Engineering
- Embeddings and semantic retrieval
- LLM evaluation and grounding techniques
- Tool function calling and agent orchestration
- AWS
- Amazon Bedrock
- Amazon SageMaker
- Experience deploying AI ML workloads within AWS environments
- LLM Integration
- Amazon Bedrock model integration and or OpenAI APIs
- Foundation model integration
- API driven GenAI applications
- Vector Search Databases
- FAISS
- Pinecone
- OpenSearch
- Or comparable vector database retrieval technology
- Recent Infosys AI hiring material similarly highlights LangGraph LangChain RAG Pinecone FAISS type vector stores prompt engineering evaluation and production AI engineering
Additional Responsibilities:
- Experience 5 15 Years
- Locations Bangalore Chennai Pune Hyderabad Trivandrum
- Employment Full time
Preferred Skills:
Technology->AI-AI Engineering->AI/ML Solution Architecture and Design->traditional ai ml