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Tier‑1 brand and metro location, but niche principal AI enablement skillset lowers candidate density.
Role requires specialized AI/LLM architectures and orchestration expertise, making cross-industry transferability limited.
Multiple mandatory advanced AI, LLM, infrastructure, and tooling skills create stringent shortlisting filters.
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Lead integration and adoption of AI-powered development tools such as Microsoft Copilot, Claude, and MCP servers within the engineering organization.
Define and establish best practices, workflows, and governance models for AI-assisted/spec-driven software development to enhance developer productivity, quality, and delivery velocity.
Build reusable frameworks and accelerators to enable scalable AI-enabled application development and rigorously evaluate AI solutions using benchmarking to track agent drift and regressions.
Expert-level proficiency in Python and hands-on experience with orchestration frameworks like LangChain, LangGraph, or similar.
Deep understanding of Retrieval-Augmented Generation (RAG) architectures, vector databases (e.g., Pinecone, Milvus), reranking mechanisms, and function-calling schemas.
Experience with cloud-native infrastructure (AWS or Azure), containerization tools (Docker, Kubernetes), and automated CI/CD pipeline design.
Work Experience Required: Not explicitly mentioned in the JD.
Principal-level engineer with strategic experience in AI/LLM architecture integration and engineering enablement at enterprise scale.
Experienced in building standards and reusable practices for AI-assisted software development within large technology organizations.
Strong background in DevOps and infrastructure automation to support AI-driven development workflows and ensure scalability and governance.