. The ideal candidate should have strong expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI architecture, and intelligent automation to drive next-generation enterprise AI transformation initiatives.
The candidate will be responsible for designing, developing, and deploying enterprise-grade GenAI and Machine Learning solutions for intelligent incident management, recommendation systems, similarity matching, resolution prediction, and root-cause analysis. The role includes building RAG-based AI applications, developing AI-powered knowledge retrieval systems, integrating AI models with enterprise platforms, defining AI architecture, and implementing continuous feedback learning mechanisms.
Professionals should have hands-on experience with Azure OpenAI, Machine Learning, Generative AI, RAG Architecture, LangChain, Semantic Kernel, Python, Vector Databases, AI model orchestration, and enterprise system integration. Strong understanding of LLMs, prompt engineering, AI agents, embeddings, semantic search, ServiceNow integration, and scalable AI solution architecture is highly preferred.
The role also involves collaborating with solution architects, data engineers, backend engineers, business stakeholders, and global delivery teams to build scalable AI platforms and intelligent automation solutions. Candidates will mentor junior AI engineers, establish AI development best practices, optimize model performance, and ensure secure, responsible, and enterprise-ready AI implementations. Strong leadership, communication, stakeholder management, analytical, and problem-solving skills are essential.
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