Remote VP of Enterprise AI Engineering

Sedgwick

United States

Remote

USD 200,000 - 320,000

Full time

9 days ago

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Job summary

Sedgwick is seeking a Vice President of Generative AI Engineering for Enterprise to set the strategy, governance, and execution model for data and AI platforms across on-premise and multi-cloud environments. You will drive enterprise AI capabilities, ensure regulatory compliance, and partner with executive leadership to deliver measurable business value.

You will lead a team responsible for ChatGPT Enterprise, Claude, and related AI services, overseeing cost governance, security, and

Qualifications

Bachelor’s degree in Computer Science, Data Engineering, Artificial Intelligence or a related field from an accredited college or university required.
Masters degree preferred.
10+ years of engineering, platform, or enterprise technology experience with 5+ years in technical leadership.

Responsibilities

Lead AI engineering for enterprise functions including HR, Marketing, Finance, Legal, and other corporate functions.
Identify and deliver high‑value LLM, agentic AI, workflow automation, and knowledge‑assistant use cases.
Build reusable AI capabilities, agents, APIs, connectors, and workflow integrations rather than isolated point solutions.
Own the enterprise administration and operational management of ChatGPT Enterprise and Claude.
Manage user access, licenses, roles, permissions, workspaces, and enterprise configuration for these platforms.
Establish and manage approved connectors and integrations to enterprise data and applications.
Partner with Security, Privacy, Legal, and AI Governance to define appropriate access controls and data-use guardrails.
Establish standards for enterprise use of GPTs, agents, projects, connectors, and other platform capabilities.
Monitor adoption, utilization, license consumption, token/API usage, and overall platform effectiveness.
Partner closely with Finance and Procurement to manage AI platform spend, forecasting, chargeback/showback, license optimization, and vendor commitments.
Develop cost-management practices to identify unused licenses, excessive consumption, duplicated capabilities, and opportunities for optimization.
Own relationships with OpenAI, Anthropic, and other relevant enterprise AI technology providers.
Evaluate new platform capabilities and determine when they should be enabled, restricted, piloted, or adopted broadly.
Ensure enterprise AI tools align with Sedgwick’s broader AI architecture and do not create unmanaged technology or data silos.
Establish operational dashboards for adoption, usage, cost, risk, and business value.
Lead a small engineering/platform team responsible for enterprise AI solutions, integrations, administration, and support.
Partner with business leaders to drive responsible adoption and measurable productivity improvement from enterprise AI.