Gen AI Developer

Job Title Senior Generative AI Developer

Experience

5–8 Years of overall software development

experience

3+ Years of hands-on

experience

in Generative AI / LLM application development Role Overview We are seeking a highly skilled Senior Generative AI Developer to design, develop, and deploy enterprise-grade Generative AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks. The ideal candidate should have strong expertise in Python, AI orchestration frameworks, cloud platforms, and modern AI application architecture. You will work closely with AI Architects, Data Scientists, Product Managers, and Full Stack Engineers to build scalable AI-powered solutions.

Requirements

Key Responsibilities

Generative AI Application Development Design and develop production-grade GenAI applications using LLMs, RAG, and Agentic AI. Build conversational AI assistants, enterprise chatbots, document intelligence platforms, and AI copilots. Develop AI-powered workflows using LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or Google ADK. Integrate OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, or AWS Bedrock models. RAG & Knowledge Management Design Retrieval-Augmented Generation (RAG) pipelines. Implement document ingestion, chunking, embedding generation, semantic search, and retrieval. Build vector search solutions using Pinecone, FAISS, ChromaDB, Weaviate, pgvector, Qdrant, or Azure AI Search. Optimize retrieval quality using hybrid search, reranking, and prompt engineering. Agentic AI Development Develop autonomous AI agents and multi-agent workflows. Build tool-calling agents and workflow orchestration. Implement Model Context Protocol (MCP) integrations. Design memory management and agent planning strategies. Backend & API Development Develop scalable backend services using Python and FastAPI. Build REST APIs and microservices for AI applications. Integrate enterprise systems and third-party APIs. Ensure secure, scalable, and high-performance AI services. Cloud & MLOps Deploy AI applications on Azure, AWS, or GCP. Containerize applications using Docker and Kubernetes. Implement CI/CD pipelines for AI deployments. Monitor LLM performance, latency, hallucinations, and production health. Collaboration Work closely with AI Architects and Product Owners. Participate in Agile ceremonies. Mentor junior AI engineers. Contribute to architecture discussions and code reviews. Required

Skills

&

Qualifications

Experience

5–8 years of software development experience. 3+ years of hands-on

experience

in Generative AI.

Experience

building production AI applications.

Key Responsibilities

Generative AI Application Development Design and develop production-grade GenAI applications using LLMs, RAG, and Agentic AI. Build conversational AI assistants, enterprise chatbots, document intelligence platforms, and AI copilots. Develop AI-powered workflows using LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or Google ADK. Integrate OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, or AWS Bedrock models. RAG & Knowledge Management Design Retrieval-Augmented Generation (RAG) pipelines. Implement document ingestion, chunking, embedding generation, semantic search, and retrieval. Build vector search solutions using Pinecone, FAISS, ChromaDB, Weaviate, pgvector, Qdrant, or Azure AI Search. Optimize retrieval quality using hybrid search, reranking, and prompt engineering. Agentic AI Development Develop autonomous AI agents and multi-agent workflows. Build tool-calling agents and workflow orchestration. Implement Model Context Protocol (MCP) integrations. Design memory management and agent planning strategies. Backend & API Development Develop scalable backend services using Python and FastAPI. Build REST APIs and microservices for AI applications. Integrate enterprise systems and third-party APIs. Ensure secure, scalable, and high-performance AI services. Cloud & MLOps Deploy AI applications on Azure, AWS, or GCP. Containerize applications using Docker and Kubernetes. Implement CI/CD pipelines for AI deployments. Monitor LLM performance, latency, hallucinations, and production health. Collaboration Work closely with AI Architects and Product Owners. Participate in Agile ceremonies. Mentor junior AI engineers. Contribute to architecture discussions and code reviews. Required

Skills

&

Qualifications

Experience

5–8 years of software development experience. 3+ years of hands-on

experience

in Generativ