Responsibilities
- 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.
- 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.
- Empower data scientists by transforming models of all maturity levels—from exploratory notebooks to advanced prototypes—into 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 accelerator—removing friction, reducing time-to-value, and enabling faster iteration cycles through automation, observability, and reusable infrastructure.
- 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.
- Work 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.
- Prioritize and promote use cases that can drive real incremental value.
- Stay at the forefront of the GenAI ecosystem—evaluating open-source and proprietary models (e.g., LLaMA, Phi) and integrating them into scalable, secure, and responsible enterprise solutions.
- 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.
- Accomplish productionization primarily in python, and deployed as containers or onto databricks.
- Champion modern engineering practices such as containerization, CI/CD, and cloud-native infrastructure.
- 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.
Requirements
- Visionary leadership and deep technical fluency.
- Builder and operator mindset—equally comfortable setting bold direction and rolling up sleeves to ensure delivery excellence.
