Jobbörse

The Director of Machine Learning & Artificial Intelligence (ML & AI) leads the enterprise's ML & AI Development and Engineering Center of Excellence (COE), serving as the central force behind our AI strategy, execution, and innovation. This role is accountable for building and scaling the COE into a world-class capability hub that delivers production-grade AI/ML solutions across the business.

As the senior-most leader of the ML & AI COE, this individual will define the strategic roadmap, architect the technical foundation, and cultivate the talent and culture necessary to accelerate enterprise-wide AI adoption. They will oversee the development of intelligent systemsfrom traditional ML models to cutting-edge generative AI agentsensuring solutions are scalable, sustainable, and aligned with business priorities.

This role requires a rare blend of visionary leadership and deep technical fluency. The ideal candidate is a builder and operator, equally comfortable setting bold direction and rolling up their sleeves to ensure delivery excellence.

Key Responsibilities

COE Leadership & Strategy

• Lead the ML & AI Center of Excellence as the enterprise's central engine for AI innovation, engineering, and enablement.
• Define and evolve the enterprise-wide ML & AI strategy in alignment with business goals and emerging technology trends.
• Serve as the organization's primary evangelist for responsible AI, driving awareness, education, and adoption across functions.
• Identify, prioritize, and champion high-impact AI opportunities that unlock business value and operational efficiency.
• Create resource plans, and track spend to budgets.

Team & Capability Building

• Build and scale a high-performing ML & AI engineering organization, including hiring, mentoring, and org design.
• Foster a culture of innovation, experimentation, and continuous learning within the COE and beyond.
• Establish and enforce best practices for ML Ops, model lifecycle management, and platform scalability.

Model Enablement & Productionization

• Empower data scientists by transforming models of all maturity levelsfrom exploratory notebooks to advanced prototypesinto robust, governed, and scalable production assets.
• Establish seamless handoff processes and shared tooling that allow data scientists to focus on experimentation and insight generation, while ML engineers ensure operational excellence, compliance, and long-term maintainability.
• Position the ML engineering function as a trusted partner and acceleratorremoving friction, reducing time-to-value, and enabling faster iteration cycles through automation, observability, and reusable infrastructure.

GenAI & Agentic Systems Innovation

• Collaborate closely with the enterprise GenAI enablement product owner to co-develop tailored agentic solutions that meet business needs and align with enterprise architecture and governance standards.
• Lead the development and integration of advanced generative AI capabilities, including tailored solutions. Working closely with consumers, and the Data engineering, quality and governance teams.
• Drive experimentation and rapid prototyping of intelligent agents that augment decision-making, automate workflows, and unlock new business capabilities. But prioritize and promote use cases that can drive real incremental value.
• Stay at the forefront of the GenAI ecosystemevaluating open-source and proprietary models (e.g., LLaMA, Phi) and integrating them into scalable, secure, and responsible enterprise solutions.

Technical Execution & Engineering Excellence

• Oversee the design, development, and deployment of custom AI agents, ML pipelines, and intelligent systems.
• Ensure seamless productionization of models with a focus on performance, reliability, and maintainability. This is primarily accomplished in python, and deployed as containers or onto databricks.
• Champion modern engineering practices such as containerization, CI/CD, and cloud-native infrastructure.

Cross-Functional Collaboration

• Partner with Data Engineering, Data Science, and Solution Architecture COEs to ensure alignment and interoperability.
• Collaborate with business stakeholders to translate complex needs into scalable, value-driven AI solutions.
• Represent the ML & AI COE in enterprise governance, architecture, and innovation forums.

Qualifications

Required

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
• 12+ years of experience in AI/ML, including 5+ years in a senior leadership role.
• Proven track record of delivering enterprise-scale ML systems in production environments.
• Deep expertise in ML Ops, model deployment, and AI platform architecture.
• Hands-on experience with GenAI technologies, LLMs, and multi-agent systems (e.g., MCP, A2A).
• Strong foundation in software engineering, cloud infrastructure, and containerization (e.g., Docker, Kubernetes).
• Exceptional communication, influence, and stakeholder management skills.

Preferred

• PhD in a relevant technical field.
• Experience with both open-source and proprietary AI models.
• Familiarity with responsible AI practices, model governance, and ethical considerations.
• Experience scaling AI capabilities in large, matrixed organizations.
• Recognized contributions to the AI/ML community (e.g., publications, open-source projects, speaking engagements).

Additional Information

Benefits: Paid Holidays and Vacation Medical, Dental & Vision benefits that start on the first day of employment No-cost mental health support for employee and dependents Childcare tuition discounts No-cost fitness, nutrition, and wellness programs Fertility benefits Adoption assistance 401k matching contributions 15% off the purchase price of stock Company bonus