Building and Evolving the AI platform - Setting the direction for a multi‑cloud AI platform that supports a wide range of workloads—from classical predictive models to modern GenAI and multi‑agent systems.
Predictive Model Enablement - A core part of the platform is making predictive modeling easier to build, deploy, and operate at scale.
Ensuring models meet expectations around performance, explainability, fairness, and auditability – critical requirements in a regulated environment.
AI Agents and Multi‑agent Systems – Leading the enablement of AI agents, including more advanced multi‑agent patterns.
Responsibilities include providing reference architectures, shared services, and guardrails so teams can build agent‑based solutions that are effective, observable, and safe.
Agentic Analytics and Conversational BI - The platform will support conversational analytics and agent‑driven insights grounded in trusted data.
Developer Experience and Enablement – This role heavily invests in developer experience, creating clear 'paved paths' for teams building models and agents.
MLOps, LLMOps, and Reliability - Running AI in production requires discipline.
Accountable for platform reliability, with clear SLOs, capacity planning, incident response, and cost visibility.
Cloud Platform Operations - Supporting and standardizing SageMaker for training, experimentation, and inference on AWS.
On Google Cloud, automating Vertex AI environments, pipelines, and deployments, using infrastructure‑as‑code and self‑service patterns.
Gemini Enterprise Enablement – The role will guide how Gemini Enterprise is adopted across the company.
Governance, Risk, and Responsible AI - Working closely with Risk, Legal, Compliance, and Security to embed governance directly into the platform.
Training and Adoption - Overseeing training and enablement for predictive modeling, AI agent development, and safe production practices.
Leadership and Influence - As a VP-level leader, this role will hire and develop strong leaders, set clear expectations, and create an environment where teams do their best work.

Requirements

15+ years of experience leading AI, ML, or platform engineering organizations at enterprise scale
Proven ability to scale teams, platforms, and operating models in complex environments
Bachelor’s or master’s degree in computer science, engineering, or a related field (preferred)
Deep understanding of how predictive models, LLMs, and AI agents behave in production
Experience operating AI systems with strong discipline around reliability, performance, and lifecycle management
Hands‑on experience designing and operating AI/ML platforms on AWS and Google Cloud
Strong working knowledge of training, experimentation, inference, and pipeline orchestration in cloud environments
Expertise across feature pipelines, evaluation metrics, CI/CD, monitoring, and rollback strategies
Ability to make and explain tradeoffs between reliability, scalability, cost, and speed
Demonstrated experience partnering with Risk, Legal, Compliance, and Security teams
Strong understanding of governance controls required in regulated enterprise environments
Ability to communicate complex technical topics clearly to senior executives and business leaders
Trusted partner to technology and business stakeholders, with sound judgment backed by data
Experience leading large, distributed teams through clear expectations and strong talent development
Proven track record of translating strategy into measurable outcomes, with accountability for execution and results.

Benefits

Health insurance
401(k) matching
Flexible work hours
Paid time off
Remote work options

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI platform developmentpredictive modelingMLOpsLLMOpsAI agentspipeline orchestrationfeature pipelinesCI/CDmonitoringrollback strategies

Soft Skills

leadershipinfluencecommunicationteam developmentstrategic thinkingjudgmentcollaborationexpectation settingproblem solvingtraining and enablement