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Strong Tier-1 brand and metro location increase candidate density despite senior, specialized AI requirements.
Specialized LLM and MLOps experience is transferable across industries but enterprise governance increases specificity.
Explicit 10+ years, mandatory AI production, cloud and tooling requirements create strict shortlisting filters.
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Lead design and delivery of secure, stable, scalable AI-powered solutions using LLMs, agents, and modern evaluation practices.
Own AI system architecture, development of prototypes, experiments, and production-ready capabilities with measurable business impact.
Drive adoption of AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational stability.
5+ years formal software engineering training or certification with 10+ years applied experience, including 3+ years delivering AI/ML and Generative AI production solutions.
Hands-on experience designing and implementing LLM solutions, retrieval-augmented generation (RAG), embeddings, agentic workflows, and prompt/model optimization.
Advanced programming skills in Python, Java, or TypeScript; experience with AI frameworks like LangChain, LangGraph, or equivalent.
3+ years building and operating cloud-native production services on AWS, Azure, or GCP; experience in MLOps/LLMOps practices including model deployment and lifecycle management.
Proven ability to architect and operationalize complex AI systems integrating multiple components (LLMs, RAG, agents) with focus on reliability and security.
Demonstrated leadership in applying AI-assisted development tools and enforcing validation standards to ensure safety, correctness, and code quality at scale.
Experience navigating AI governance, data sensitivity, and secure engineering workflows to enable compliant, resilient AI adoption in enterprise environments.