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Metro, mid-level AI role with moderate brand and specialized enterprise AI skillset.
Requires enterprise AI, RAG, and Azure production experience, limiting cross-industry portability.
Explicit 6+ years and enterprise-grade Azure, RAG, MLOps requirements make screening highly selective.
Own end-to-end architecture of agentic and retrieval augmented generation (RAG) AI systems on Azure, including data pipelines, agent workflows, prompt systems, and APIs.
Define and enforce evaluation standards, production-readiness, reliability, security, compliance, and MLOps for AI features impacting live enterprise customers' career decisions.
Mentor engineers, collaborate with product teams, and ensure responsible AI practices (fairness, transparency, explainability) are embedded in the AI platform.
6–8+ years of experience in software, ML, or AI engineering with independent judgment on high-stakes problems.
Deep enterprise-grade experience architecting RAG, multi-agent orchestration, vector search, and retrieval systems under real constraints (security, compliance, multi-tenant).
Strong proficiency with Python and modern AI stack (PyTorch/TensorFlow, Hugging Face, scikit-learn, Pandas, NumPy).
Must be India-based, able to work onsite full-time in Noida.
Experienced architect and technical owner of production AI/ML systems accountable for system reliability and evolution at enterprise scale.
Demonstrates evaluation mindset by establishing metrics, evaluation harnesses, and shipping standards across teams.
Fluent with MLOps (Docker, Kubernetes, CI/CD) on Azure and capable of communicating complex technical tradeoffs to non-technical stakeholders.