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Strong employer brand and metro location but senior, specialized role reduces applicant competition to medium.
Advanced ML engineering skills transfer, but biotech forecasting and regulated expertise create high domain bias.
Explicit 12+ years experience, deep ML/MLOps requirements, and regulated domain make filters highly stringent.
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Set technical strategy and lead design, deployment, and scaling of enterprise ML, LLM, and agentic AI systems for forecasting, decision support, and operational automation across multiple business units.
Translate complex forecasting and AI agent patterns into reliable, governed production solutions that impact critical business processes and enterprise-wide priorities.
Establish robust MLOps capabilities including CI/CD, model lifecycle management, governance, and operational excellence in high-impact regulated environments.
Degree with 12+ years in machine learning engineering, software engineering, data science engineering, or related quantitative discipline.
10+ years building, deploying, and operating production ML/AI/data/software systems, including experience as a technical lead on complex initiatives.
Strong programming in Python and SQL; experience with ML frameworks (e.g., PyTorch, TensorFlow), MLOps tools, cloud platforms, containerization, CI/CD, and distributed systems.
Deep hands-on experience with full ML lifecycle, forecasting, Bayesian and probabilistic models, NLP/LLM systems in production environments.
Experienced principal-level ML engineer with background in enterprise forecasting, supply chain, manufacturing, commercial analytics, or regulated operational environments.
Skilled at navigating ambiguity, influencing cross-functional teams, and delivering measurable business value in matrixed enterprise organizations.
Able to architect scalable, secure AI agent workflows combining forecasting, decision automation, and human-in-the-loop controls in production.