Senior Manager, AI Engineering

Position Summary

Senior Manager, AI Engineering to advance enterprise AI capabilities through development, deployment, and governance of scalable AI/ML solutions, partnering with R&D, Commercial, and Corporate teams to turn data into actionable insights and business value.

• Execute enterprise AI strategy/roadmap; align AI/ML initiatives with business priorities and identify high-value use cases.
• Develop AI/ML solutions across R&D, Commercial, and Corporate functions.
• Deliver analytics and data science use cases for data-driven decision-making.
• Support AI governance with technical expertise on model risk, responsible AI practices, and lifecycle management.
• Assess feasibility, business value, and implementation risks for prioritization.
• Design, develop, validate, deploy, and support ML models and scalable ML/LLM pipelines using ML Ops/LLM Ops.
• Evaluate, implement, and optimize GenAI (LLMs, vector databases, endpoints, agent frameworks, guardrails) per enterprise architecture.
• Maintain model/prompt/dataset documentation for lineage, auditability, governance compliance, approvals, and system-of-record standards.
• Build reusable frameworks, reference implementations, and technical standards to improve scalability/efficiency.
• Partner with IT, Data Insights & Analytics, and Info Sec for AI platform support, infrastructure, vendor assessments, and RFI/RFPs.
• Apply governance standards (lifecycle management, bias/robustness testing, explainability, human oversight, incident response, regulatory compliance).
• Support compliance with GxP and evolving AI regulations (e.g., NIST AI RMF, EU AI Act readiness).
• Contribute to AI enablement (knowledge sharing, documentation, education, portfolio reviews).
• Monitor emerging AI/ML/regulatory developments; recommend architectures, platform strategy, and build-vs-buy.

• MS in quantitative field + 5+ years relevant experience; or PhD + 2+ years industry (or equivalent research) in ML/AI/data science.
• Production or research experience deploying/supporting ML/AI.
• Proficiency:
  Python, SQL; frameworks such as PyTorch/Tensor Flow/scikit-learn.
• GenAI experience: LLMs, RAG, vector databases, agent frameworks.
• ML Ops/LLM Ops experience (version control, evaluation, monitoring, deployment).
• Knowledge of responsible AI, explainability, evaluation methods, and AI governance.
• Regulated/compliance-focused environment experience preferred.
• Domestic/international travel as needed.

• Discretionary bonus and equity awards eligibility.
• Salary range: $150,300—$187,900 USD.
• Competitive base/bonus/equity; medical/dental/vision; life/disability/business travel/EAP; 401(k) match 1:1 up to 5%; ESPP; 15+ vacation days; 13–15 paid holidays; paid sick time; paid parental leave; tuition assistance.