VP AI Engineering - Enterprise

Sedgwick•Telecommuter FL, FL

3d•Remote

About The Position

The Vice President, Generative (Gen) AI Engineering Enterprise, provides executive leadership, strategic direction, and accountability for the enterprise data and AI engineering ecosystem that enables the organization’s most advanced analytics, data science, and generative AI initiatives. This role owns the vision, governance, and execution model for the enterprise data supply chain that powers machine learning platforms, feature stores, and production‑grade generative AI capabilities. As a senior leader, the Vice President establishes and scales the engineering strategy required to transform legacy and modern data assets into trusted, AI‑ready enterprise capabilities. The role ensures that data and AI engineering investments are aligned to business outcomes, regulatory expectations, and long‑term architectural sustainability across on‑premise and multi‑cloud environments, including Snowflake and AWS/Azure AI ecosystems.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Artificial Intelligence or a related field from an accredited college or university required.
10+ years of engineering, platform, or enterprise technology experience with 5+ years in technical leadership.
Strong hands-on understanding of LLMs, agentic AI, RAG, APIs, workflow automation, and enterprise integration.
Experience building AI solutions for corporate functions such as HR, Marketing, Finance, or Legal.
Experience administering enterprise SaaS or AI platforms at significant organizational scale.
Strong understanding of identity, SSO, RBAC, connectors, APIs, data permissions, and enterprise security.
Strong AWS and/or Azure knowledge and understanding of how enterprise AI platforms integrate with cloud environments.
Experience establishing AI governance, access controls, monitoring, and responsible-use standards.
Strong financial management skills with experience managing technology consumption, licenses, vendor spend, and forecasts.
Ability to work effectively across Engineering, Security, Finance, Procurement, Legal, HR, Marketing, and executive leadership.
Ten (10) years of progressive experience in data engineering, analytics platforms, or AI‑related technologies, including senior leadership responsibility for enterprise‑scale data or AI initiatives, or an equivalent combination of education and experience required.
Executive‑level expertise in modern data architectures, cloud data platforms, and AI enablement, including Snowflake and AWS/Azure ecosystems
Proven experience establishing enterprise governance models for data, analytics, and AI systems
Strong understanding of machine learning and generative AI data requirements, including feature engineering, vector databases, and RAG‑based architectures
Ability to set technical direction without direct hands‑on execution, guiding senior engineers and leaders through complex architectural and delivery decisions
Experience managing large, cross‑functional initiatives with significant financial and organizational impact
Strong financial acumen, including budgeting, investment prioritization, and ROI measurement for technology initiatives
Exceptional executive communication skills, with the ability to influence senior leaders and align stakeholders across business and technology domains
Demonstrated ability to lead through ambiguity, scale new capabilities, and drive enterprise change

Nice To Haves

Masters Degree preferred.
Experience with ChatGPT Enterprise, Claude Enterprise, Microsoft Copilot, or comparable enterprise AI platforms strongly preferred.

Responsibilities

Defines and leads the enterprise strategy for data and generative AI engineering, establishing standards, roadmaps, and operating models that support analytics, machine learning, and AI at scale.
Provides executive oversight for the design, delivery, and operation of enterprise ETL/ELT platforms that integrate legacy on‑premise systems (including mainframe, SQL Server, and DB2) with modern cloud data and AI platforms.
Establishes governance and accountability for feature stores, analytically optimized datasets, and AI‑ready data products to ensure reproducibility, regulatory compliance, and production reliability.
Sets direction for generative AI enablement, including enterprise approaches for vectorization, embeddings, retrieval‑augmented generation (RAG), and secure consumption of structured and unstructured data.
Owns the enterprise Snowflake strategy, including architectural patterns, performance standards, cost governance, and integration with cloud‑based AI services across AWS and Azure.
Leads cross‑functional collaboration with Technology, Security, Infrastructure, Legal, Risk, and Compliance leaders to ensure data and AI engineering practices meet security, privacy, and regulatory requirements.
Establishes executive‑level operating metrics and financial discipline to ensure data and AI engineering investments deliver measurable business value and return on investment.
Provides leadership and direction to engineering leaders and teams, setting priorities, removing organizational barriers, and ensuring consistent execution across portfolios and initiatives.
Serves as a senior advisor to executive leadership on data, AI, and generative AI capabilities, risks, and opportunities, translating technical complexity into business‑level insights and decisions.
Drives organizational readiness by shaping talent strategy, capability development, and succession planning for data and AI engineering roles across the enterprise.
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.
Performs other duties as assigned.
Travel as required.