VP of AI Platform

Overview

VP IT Management - IM04AE

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.

At The Hartford, we’re building the next generation of AI capabilities that power real business decisions—from predictive models that shape risk and pricing to AI agents that help people work smarter. To lead this effort, we are seeking a Vice President of AI Platform who brings together strong engineering discipline, practical innovation, and sound governance.

This role sits at the center of the company’s AI strategy and is responsible for shaping and operating an enterprise AI platform that supports predictive modeling, generative AI, and agent‑based systems, operating reliably across AWS and Google Cloud. Equally important, the role ensures these capabilities are safe, well governed, and trusted by the business.

The Vice President of AI Platform leads a senior organization of platform engineers, MLOps and reliability specialists, and enablement leaders, partnering closely with Data & Analytics, Security, Risk, Legal, and business leaders to enable rapid delivery aligned with enterprise standards.

This role can have a Hybrid or Remote work arrangement. Candidates who live near one of our office locations (Hartford, CT; Charlotte, NC; Chicago, IL; Columbus, OH) will be expected to work in an office three days per week (Tuesday through Thursday). Candidates who do not live near an office will have a remote work arrangement, with the expectation of coming into an office as business needs arise.

Candidates must be authorized to work in the U.S. without company sponsorship.

• 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. This includes establishing clear architectural standards for security, data access, identity, and deployment, while still giving teams the flexibility they need to deliver.
• Predictive Model Enablement - A core part of the platform is making predictive modeling easier to build, deploy, and operate s role will oversee standardized pipelines for features, training, validation, deployment, and monitoring. 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 where agents collaborate, review each other’s work, or operate with human oversight. 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. This role will help establish and scale a strong semantic layer using Looker and/or Snowflake so metrics, dimensions, and predictions remain consistent, whether they’re surfaced in dashboards or through natural‑language interactions.
• Developer Experience and Enablement – This role heavily invests in developer experience, creating clear “paved paths” for teams building models and agents. This includes templates, APIs, and tooling, such as Antigravity and the Gemini CLI—that shorten the path from idea to production and reduce one‑off engineering work.
• MLOps, LLMOps, and Reliability - Running AI in production requires discipline. This leader will ensure strong practices around CI/CD, versioning, evaluation, monitoring, and rollback for both models and agents. Accountable for platform reliability, with clear SLOs, capacity planning, incident response, and cost visibility.
• Cloud Platform Operations - Supporting and standardizing Sage Maker for training, experimentation, and inference on AWS. On Google Cloud, automating Vertex AI environments, pipelines,…