JOB SUMMARY We are seeking a highly skilled GenAI Developer with strong expertise in Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI frameworks. The ideal candidate will design, develop, and deploy AI-powered applications leveraging Large Language Models (LLMs), intelligent agents, vector databases, and cloud-native AI services. This role requires hands-on experience building scalable AI solutions that automate business processes and deliver advanced conversational experiences. Key Responsibilities Design and develop GenAI applications using LLMs, RAG architectures, and Agentic AI frameworks. Build intelligent AI agents capable of planning, reasoning, tool usage, and multi-step task execution. Develop and optimize RAG pipelines using vector databases and embedding models. Integrate OpenAI, Azure OpenAI, Anthropic, Gemini, or open-source LLMs into enterprise applications. Create AI-powered chatbots, copilots, virtual assistants, and workflow automation solutions. Fine-tune prompts, evaluate model performance, and improve response quality. Implement orchestration frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel. Develop scalable APIs and backend services using Python. Collaborate with data engineers, architects, and business stakeholders to deliver AI-driven solutions. Ensure AI security, governance, observability, and responsible AI practices. Required Qualifications Strong experience in Generative AI and Large Language Models (LLMs). Hands-on expertise with RAG (Retrieval-Augmented Generation) architecture. Strong experience in Agentic AI frameworks such as LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, or Semantic Kernel. Advanced programming skills in Python. Experience with vector databases such as Pinecone, Weaviate, ChromaDB, FAISS, or Milvus. Strong understanding of embeddings, prompt engineering, and model evaluation. Experience integrating LLMs through APIs and cloud platforms. Knowledge of REST APIs, Microservices, and scalable backend development. Experience with cloud platforms such as AWS, Azure, or GCP. Preferred Qualifications Experience with Azure OpenAI Services. Exposure to fine-tuning, model training, and MLOps. Experience with knowledge graphs and multi-agent systems. Familiarity with Kubernetes, Docker, and CI/CD pipelines. Experience building AI applications for Banking, Financial Services, or Enterprise domains. Technologies GenAI: OpenAI, Azure OpenAI, Claude, Gemini, Llama Frameworks: LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel Programming: Python, FastAPI, REST APIs Data: Vector Databases, SQL/NoSQL Databases Cloud: AWS, Azure, GCP