Senior Manager, AIML Engineering

JOB SUMMARY:

The Senior Manager AI/ML Engineering will lead a multidisciplinary team of data scientists AI/ML engineers data engineers and developers to design and deploy enterprise-scale optimization and decision-intelligence solutions that drive measurable business outcomes. This role combines strategic vision with hands-on technical leadership serving as an enterprise subject matter expert for machine learning optimization and mathematical programming while guiding teams to deliver production-ready systems that enhance business performance and guest experiences. Key responsibilities include managing a portfolio of applied AI/ML initiatives across personalization marketing dynamic pricing revenue management workforce planning digital experiences and other complex decision domains; translating strategy into execution; embedding responsible AI and decision-system practices; and fostering a culture of innovation. The ideal candidate will be a collaborative leader committed to developing talent championing technical rigor and enabling AIML and optimization at scale across the organization.

MAJOR RESPONSIBILITIES:

Strategic Leadership & Enterprise Enablement

• Provide strategic and technical leadership to multidisciplinary teams of data scientists AI/ML engineers data engineers and developers setting direction and aligning work to enterprise priorities while promoting collaboration continuous learning and high-impact delivery.
• Establish and communicate a clear enterprise vision technical roadmap and operating model for optimization and decision intelligence balancing immediate business needs with long-term capability development.
• Direct technical teams by defining objectives decision rights technical standards architectural guardrails and measurable outcomes.
• Guide and mentor technical teams through problem formulation architecture model design solution trade-offs and production readiness in support of machine learning optimization and mathematical programming.
• Coordinate initiative sequencing resource needs dependencies and delivery risks across product engineering data and business teams removing roadblocks and ensuring execution remains aligned with scope schedule and business outcomes.

Applied AI/ML & Optimization Development

• Provide technical direction and oversight for AI/ML and optimization development architecture code quality reusable libraries reference architectures and product-lifecycle best practices to ensure scalable maintainable secure and production-ready solutions.
• Engage with internal and external partners to gather requirements and collaborate with engineers architects product leaders and business stakeholders to design robust solutions across domains.
• Lead end-to-end problem formulation model design algorithm selection prototyping benchmarking and validation across optimization machine learning and decision science. Apply advanced analytical and AI/ML techniques to integrate predictive and prescriptive modeling experimentation simulation real-time signals and business constraints into scalable decision systems that improve business outcomes and adapt over time.
• Establish delivery and operational governance remove cross-team roadblocks and track quality reliability adoption and business-impact KPIs. Define standards for model and solver monitoring performance degradation and drift detection incident response and continuous improvement.

AI/ML Technical Strategy

• Define and drive the strategy and prioritized portfolio for optimization products and decision systems balancing near-term business outcomes with long-term platform evolution and determining where capabilities should be built bought reused or standardized across personalization marketing dynamic pricing revenue management workforce planning digital experiences and resource allocation.
• Evaluate open-source and commercial solvers frameworks and platforms across performance cost scalability licensing and security; direct cross-functional alignment on technology choices investments standards and responsible-AI requirements and communicate recommendations trade-offs and program status to senior leadership.
• Understands and actively participates in Environmental Health & Safety responsibilities by following established UO policy procedures training and team member involvement activities.
• Performs other duties as assigned.

QUALIFICATIONS:

• Hands-on experience designing and developing production decision systems including data and feature pipelines APIs and services cloud platforms such as AWS GCP or Azure version control automated testing CI/CD containerization observability model monitoring drift detection and retraining or recalibration processes.
• Deep expertise in statistical inference supervised and unsupervised learning regression classification clustering and segmentation time-series forecasting probabilistic modeling predictive analytics and model ensembling.
• Demonstrated expertise formulating and solving large-scale decision problems using linear mixed-integer nonlinear stochastic robust and/or multi-objective optimization; experience with constraint programming decomposition simulation heuristics and learning-based decision methods.
• Advanced proficiency in Python and SQL with experience using optimization modeling frameworks and one or more solver ecosystems such as Gurobi or CPLEX.
• Experience integrating optimization with machine learning forecasting predictive models personalization and recommendation systems experimentation simulation or adaptive decision methods.
• Proven ability to establish technical vision influence and direct multidisciplinary teams without formal authority drive cross-functional alignment and resolve complex technical and business decisions.
• Track record of connecting technical solutions to tangible business value and managing a portfolio of initiatives across multiple business domains.
• Storyteller comfortable presenting to executives and mentoring both technical and non-technical audiences.
• Hands-on experience applying privacy security fairness explainability governance and compliance principles in production AI/ML and optimization systems.
• Advanced knowledge of product and delivery methodologies with demonstrated success translating strategy into coordinated execution in fast-paced ambiguous and matrixed environments.

EDUCATION:

• Masters degree in Computer Science Engineering Data Science or in a relevant applied quantitative field is required.

EXPERIENCE:

• 9 years experience in appliedAI data science or softwareengineering roles;
• or equivalent combination of education and experience.

Your talent skills and experience will be rewarded with a competitive compensation package.

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