Description

What You'll Do
 

• Build and deploy GenAI applications using LLMs (OpenAI, Azure OpenAI, Claude, Gemini, Llama, etc.).
• Develop Agentic AI workflows using frameworks such as CrewAI, AutoGen, LangGraph, or LangChain Agents.
• Design and implement RAG pipelines, vector search solutions, and embedding‑based retrieval systems.
• Build scalable AI services using Python, FastAPI/Flask, and cloud platforms (Azure/AWS/GCP).
• Collaborate with cross‑functional teams to define use cases and convert them into production‑ready GenAI solutions.
• Implement hallucination reduction, prompt‑engineering strategies, and model evaluation methods.
• Integrate LLMs with enterprise applications, APIs, and automation workflows.
• Work with vector databases (FAISS, Pinecone, Chroma, Weaviate) for semantic search.
• Monitor, evaluate, and optimize GenAI models for accuracy, performance, and cost.
  
   

Expertise You'll Bring
 

• 5–10+ years of experience in Python-based backend engineering, AI/ML, and enterprise-grade system development.
• Strong hands-on expertise in Generative AI, LLMs, RAG pipelines, and Agentic frameworks such as LangChain, LangGraph, ADK, and similar.
• Design, build, and scale production-ready AI services, APIs, workflows, and agent-based automation using FastAPI/Flask.
• Experience integrating LLMs (OpenAI, Azure OpenAI, Claude, etc.), vector databases, and embeddings for enterprise use cases.
• Strong understanding of distributed systems, async processing, workflows, Kafka/queues, and backend scalability.
• Hands-on exposure to cloud platforms (Azure/AWS/GCP), CI/CD, and MLOps/LLMOps practices.
• Ability to own end-to-end solution design, from architecture to deployment and monitoring.
• Experience working in Agile delivery models, collaborating with product, architecture, and platform teams.
• Exposure to regulated or enterprise domains (e.g., Banking/KYC/Onboarding) is a plus

Key Skills

Education

Any Graduate