AI/ML Developer

AI/ML Developer

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 Experience: 5–15 Years Locations: Bangalore | Chennai | Pune | Hyderabad | Trivandrum Employment: Full-time 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