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Large employer brand and metro location increase applicant density, but specialized GenAI+MLOps seniority limits it.
Role demands deep ML/GenAI, production MLOps, and LLM engineering expertise, making cross-industry transferability low.
Explicit 7+ years, demonstrable GenAI shipping, production MLOps, and cloud deployment requirements create high filter strictness.
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Own end-to-end lifecycle of production-grade ML and Generative AI systems including model development, scalable cloud deployment (Azure), and performance monitoring.
Design and deliver ML models (including deep learning and GenAI like LLM applications) with strong engineering practices, CI/CD, MLOps, and automated quality controls.
Partner with senior commercial stakeholders to translate data into decision-ready insights, build analytics products, dashboards and ensure data governance with measurable business impact.
7–12+ years hands-on experience in Data Science / ML Engineering with end-to-end production deployment ownership.
Proficient in Python (production-quality coding), SQL, ML frameworks (PyTorch/TensorFlow), GenAI implementation (RAG, MCP, fine-tuning, embeddings, LLM apps).
Experience in deploying and operating ML/LLM solutions on Azure cloud including CI/CD, containerization, and monitoring.
Bachelor’s degree in engineering, CS, Statistics, Economics, Mathematics or related quantitative field; Master’s preferred.
Proven track record managing full analytics domains from experimentation to production with reproducible pipelines and operational readiness.
Strong experience delivering GenAI solutions specifically LLM applications with safety/quality controls and scalable deployment.
Skilled at partnering with senior leaders across business units to influence insight-led decision-making and storytelling in complex matrixed environments.