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Strong brand, metro location, and mid-level experience requirement increase applicant competition.
Highly specialized GenAI, LLMOps, and vector database skills limit cross-industry transferability.
Explicit 4+ years plus mandatory GenAI/LLM, cloud, and platform skills enforce stringent filters.
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Lead design and implementation of enterprise-scale Generative AI and Agentic AI solutions, including multi-agent systems, RAG architectures, AI copilots, and AI workflow automation.
Own end-to-end AI product development lifecycle from ideation to production deployment, establishing AI engineering standards, evaluation frameworks, guardrails, and governance.
Collaborate with clients and cross-functional teams (Data Engineering, Cloud, Cyber, Business) to define AI strategy, use cases, roadmaps, and deliver integrated scalable AI platforms using modern MLOps/LLMOps and cloud-native services.
Minimum 4+ years overall work experience with at least 3+ years specifically in Generative AI (GenAI) or Large Language Model (LLM) engineering.
Bachelor's degree (B.E./B.Tech.) or Master's (M.E./M.Tech./MS) in Computer Science, Artificial Intelligence, Data Science, Electrical Engineering, Mathematics, Statistics, or related; strong preference for Tier-1 institutes (IITs, IISc, IIITs, BITS, NITs).
Hands-on experience with LLMs, Agentic AI, multi-agent architectures, RAG frameworks, prompt engineering, AI evaluation/governance, vector databases, and knowledge retrieval.
Proficiency in Python and software engineering; familiarity with LangChain, LangGraph, AutoGen, CrewAI, Azure OpenAI, OpenAI, Anthropic Claude, Gemini, Azure, AWS, GCP, MLflow, Databricks, Fabric, Kubernetes, and experience with AI Copilot and enterprise AI platforms.
Demonstrated ability to lead AI engineering initiatives including architecture design, standards setting, and mentoring technical teams.
Experience in consulting or client-facing roles shaping AI strategy, product roadmaps, and implementation plans for enterprise clients.
Technical breadth across cloud platforms (Azure, AWS, GCP), modern AI toolkits, and strong software engineering background enabling end-to-end AI product delivery in enterprise environments.