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Tier-1 brand, metro, mid-level GenAI role with broad toolset attracts high applicant competition.
GenAI platform and LLMOps skills transfer across industries but need specific enterprise and tooling experience.
Explicit 5–8 years, 3+ years GenAI, and comprehensive mandatory tech stack raise strictness.
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Lead architecture and implementation of enterprise-scale Generative AI and Agentic AI solutions including multi-agent systems, RAG architectures, and AI workflow automation platforms.
Own end-to-end AI product development lifecycle from ideation through to production deployment and establish AI engineering standards, governance, and evaluation frameworks.
Collaborate with clients and cross-functional teams to define AI strategies, use cases, and deliver integrated AI solutions using cloud-native services and modern MLOps/LLMOps practices.
5+ years of technology experience with at least 3 years focused on Generative AI / LLM engineering.
Strong expertise in LLMs, Agentic AI, RAG frameworks, prompt engineering, AI governance, and vector databases.
Hands-on experience with LangChain, LangGraph, AutoGen, CrewAI, Azure OpenAI, OpenAI, Anthropic Claude, Gemini, major cloud platforms (Azure, AWS, GCP), MLflow, Databricks, Fabric, Kubernetes, and Python software engineering.
Bachelor's degree in Technology (BE/BTech) or MCA/MBA.
Experienced in leading AI engineering teams and driving AI product development in enterprise settings.
Proficient in defining AI strategy and collaborating with multi-disciplinary teams including Data Engineering, Cloud, Cybersecurity, and Business stakeholders.
Familiarity with AI product management, solution architecture, multimodal AI, document intelligence, enterprise search, and possession of relevant AI certifications (Azure AI, AWS AI/ML, Google AI, OpenAI, Anthropic) preferred.