Artificial Intelligence and Machine Learning Developer

Infosys; Bengaluru; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Infosys seeks an AI/ML Developer to design and deploy end-to-end ML and Generative AI pipelines on AWS using SageMaker and Bedrock. You will build intelligent LLM-powered agents and RAG solutions, integrating foundation models into scalable enterprise applications.

Collaborate with data engineers and cloud/platform teams to move AI solutions from prototype to production, implementing MLOps, evaluation, grounding, and responsible AI guardrails.

Qualifications

• 5–15 years of experience in AI/ML development and deployment.
• Strong hands-on Python and ML frameworks with production delivery.
• Experience implementing ML pipelines, MLOps, and LLM integration.
• Familiarity with vector stores (FAISS, Pinecone, OpenSearch) and RAG.
• Proficient in AWS Bedrock/SageMaker and OpenAI APIs.

Responsibilities

• Design and deploy end-to-end ML and Generative AI pipelines on AWS.
• Build LLM-powered agents and Agentic AI workflows using LangGraph/LangChain.
• Architect production-ready RAG solutions for document ingestion and retrieval.
• Develop scalable Python-based APIs to expose AI capabilities.
• Collaborate with data engineers and cloud/platform teams to move solutions to production.
• Apply MLOps/LLMOps principles for deployment, monitoring, and lifecycle management.

Skills

Python

ML

Scikit-learn

TensorFlow

PyTorch

ML pipelines

MLOps

LangGraph

LangChain

RAG

Prompt engineering

Vector databases

FAISS

Pinecone

OpenAI API

AWS Bedrock

SageMaker

LLM integration

Bedrock integration

OpenAI integration

Tools

SageMaker

Bedrock

OpenAI API

Job description

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
• AWS 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