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Tier-1 brand, mid-level generalist ML role, and metro location create highly competitive applicant density.
Core ML engineering skills are transferable but procurement and enterprise integration needs add moderate domain specificity.
Explicit 3–5 year requirement plus extensive mandatory LLM, MLOps, cloud and production skills indicates high strictness.
Own end-to-end design, development, and production deployment of AI/ML solutions, including Generative AI, LLMs, and traditional ML, focused on Purchasing use cases.
Drive integration of AI solutions with enterprise data platforms and procurement systems, ensuring scalability, security, and operational reliability.
Lead technical standards, mentor team members, and influence AI architecture and practices within the Purchasing AI Hub.
3–5 years of hands-on experience designing, building, and deploying production-grade AI/ML solutions.
Expertise in LLM orchestration frameworks (e.g., LangChain), prompt engineering, and RAG techniques with vector databases and knowledge graphs.
Strong skills in Python (3.11+), cloud platforms (preferably Azure), data engineering including SQL and Spark, and MLOps practices.
Experience with software engineering best practices including Git, CI/CD, Docker/Kubernetes, and REST API design.
Experienced AI engineer with comprehensive expertise across the full AI lifecycle and strong technical leadership in production-grade AI deployments.
Deep understanding of enterprise procurement or purchasing domain AI applications and relevant data integration challenges.
Comfortable working autonomously in cross-functional, agile teams delivering scalable, secure AI solutions with business impact.