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Strong employer brand and metro location, but specialized GenAI/MLOps skills limit broad applicant competition.
Deep GenAI, LLM, MLOps, and Azure enterprise architecture requirements create high background sensitivity across industries.
Explicit 8–10 years, mandated AI architecture experience and specific GenAI/MLOps tech create high shortlisting strictness.
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Own design and productionization of enterprise AI and Generative AI architectures, focusing on Agentic AI frameworks and RAG pipeline implementations.
Develop, optimize, and deploy GenAI applications using Azure OpenAI ecosystem, including prompt engineering and LLM optimization.
Lead MLOps pipeline design and AIOps monitoring integration to ensure scalable and governed AI solution delivery.
8+ years overall IT experience with 3-5 years specifically in AI/ML architecture roles.
Mandatory skills: Agentic AI frameworks (AutoGen, CrewAI, LangGraph, Semantic Kernel), RAG pipeline design, Azure OpenAI & AI ecosystem, Python programming, ML frameworks (PyTorch, TensorFlow, Scikit-learn), REST APIs, containerization (Docker, Kubernetes), MLOps pipeline experience.
Bachelor's degree in Engineering (B.E./B.Tech) mandatory.
Preferred: Azure AI Engineer Associate or Azure Solutions Architect certification; enterprise AI copilot/assistant build experience.
Experienced in designing scalable, production-grade AI architectures within large enterprises or consulting environments.
Technical leadership in deploying GenAI solutions with strong expertise in Azure AI platforms and enterprise data architecture patterns (e.g., Lakehouse, Data Mesh).
Skilled in integrating MLOps and AIOps practices to ensure responsible AI governance and monitoring in hybrid cloud settings.