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Models (LLMs).\n\nThe ideal candidate will have hands-on expertise in Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), multi-agent architectures, and AI orchestration frameworks.\n\nThis role involves building intelligent AI assistants, enterprise copilots, autonomous workflows, and scalable AI platforms that drive business innovation and operational efficiency.\n\nStrategic Responsibilities : \n\n- Design, develop, and deploy enterprise-grade applications powered by Large Language Models (LLMs) and foundation models.\n\n- Build Retrieval-Augmented Generation (RAG) solutions leveraging vector databases, enterprise knowledge repositories, and GraphRAG architectures.\n\n- Develop conversational AI assistants, enterprise copilots, intelligent chatbots, and workflow automation solutions.\n\n- Implement advanced prompt engineering techniques including Chain of Thought (CoT), ReAct, Self-Reflection, Multi-Step Reasoning, and Tool Calling.\n\n- Design and develop multi-agent AI systems capable of autonomous planning, reasoning, task decomposition, and execution.\n\n- Build agent orchestration frameworks integrating enterprise applications, APIs, and external services.\n\n- Implement agent memory management, communication protocols, and human-in-the-loop workflows.\n\n- Develop scalable AI services, APIs, and reusable AI platform components for enterprise deployments.\n\n- Integrate AI solutions with enterprise platforms including CRM, ERP, Supply Chain, Data Warehouses, and Knowledge Management systems.\n\n- Design and implement AI governance, monitoring, observability, and evaluation frameworks.\n\n- Evaluate AI applications for relevance, groundedness, hallucination, safety, accuracy, and overall performance.\n\n- Build automated evaluation pipelines, testing frameworks, and continuous model validation processes.\n\n- Optimize inference performance, latency, scalability, token utilization, and infrastructure costs.\n\n- Collaborate with Product, 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