Gen AI / Agentic AI Lead

Infosys is seeking a hands-on Gen AI / Agentic AI Lead to drive the development and deployment of next-generation AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks. This role is ideal for a mid-level engineer with strong technical depth, a passion for building, and the ability to lead small teams or workstreams in a fast-paced, innovation-driven environment.

Required Qualifications

• Bachelor's degree in Computer Science, AI/ML, or related field.
  
• 4 years of experience in software engineering or data science, with 2-3 years in Gen AI or LLM-based systems.
  
• Strong Python programming skills and experience with ML/AI libraries (Hugging Face Transformers, LangChain, PyTorch).
  
• Hands-on experience with vector databases (FAISS, Pinecone, Weaviate, Azure AI Search).
  
• Familiarity with cloud platforms and Gen AI services (AWS, Azure, GCP).
  
• Experience with REST API development (FastAPI, Flask) and containerization (Docker).
  
• Solid understanding of AI governance, model safety, and prompt engineering.
  
• This position is located in Bridgewater, NJ; Sunnyvale, CA; Austin, TX; Raleigh, NC; Richardson, TX; Tempe, AZ; Phoenix, AZ; Charlotte, NC; Houston, TX; Denver, CO; Hartford, CT; New York, NY, Palm Beach, FL; Tampa, FL or Alpharetta, GA, or is willing to relocate.
  
• Candidates authorized to work for any employer in the United States without employer-based visa sponsorship are welcome to apply. Infosys is unable to provide immigration sponsorship for this role at this time
  

• Design, develop, and deploy Gen AI applications using LLMs and agentic frameworks (e.g., LangGraph, AutoGen, Crew AI).
  
• Fine-tune open-source and proprietary LLMs using techniques like LoRA, QLoRA, and PEFT.
  
• Build and optimize RAG pipelines with hybrid retrieval, semantic chunking, and vector search.
  
• Integrate Gen AI solutions with cloud-native services (AWS Bedrock, Azure OpenAI, GCP Vertex AI).
  
• Work with unstructured data (PDFs, HTML, audio, images) and multimodal models.
  
• Implement LLMOps practices including prompt versioning, caching, observability, and cost tracking.
  
• Evaluate model performance using tools like RAGAS, DeepEval, and FMeval.

• Exposure to agentic workflows and autonomous agents.
  
• Experience with CI/CD pipelines and DevOps tools (GitHub Actions, Jenkins, Terraform).
  
• Familiarity with front-end integration (React, Angular, TypeScript) and GraphQL APIs.
  
• Knowledge of model interpretability, bias mitigation, and human-in-the-loop systems.
  
• Experience with multimodal models and perception systems (e.g., vision + language).