AI Data Engineering Lead, Vice President

About this roleThe VP AI Data Engineering Lead operates at the intersection of strategic influence, team leadership, and delivery excellence — playing a defining role in how AI-powered data and knowledge products are conceived, designed, and executed across the organization.He/She/They lead a multi-team organization of AI agent engineers and data scientists — setting technical and delivery direction, building the team’s capability, and holding accountability for the production systems powering the commercialized knowledge products.He/She/They function as the most senior bridge between Product Management leadership, business functional stakeholders, and AI engineering — bringing the technical depth and strategic breadth to influence product vision while holding firm accountability for engineering delivery outcomes across multiple squads.He/She/They sets the organizational standard for how product intent gets translated into technical reality, establishing engineering frameworks, authoring principles, and driving the culture of quality, accuracy, and ownership across the broader Product Lead community.He/She/They are deeply experienced in the nuances of AI engineering, from agent workflow design and Vision AI evaluation to production validation, output-quality governance, and customer adoption of knowledge products. Beyond the delivery mandate, they are a builder of people and of technical leaders of tomorrow — developing high-performing teams and creating the conditions for others to do their best work.This is a role for a leader who thinks in systems, acts with purpose, and measures their success not just by what ships, but by the commercial credibility and lasting organizational capability left behind.Roles & ResponsibilitiesLead a team of AI agent engineers and data scientists — setting technical direction, managing delivery, driving performance, developing individual capability across the team, and building the talent bench across the function.Own end-to-end solution design & delivery for complex AI features across multiple AI engineering squads building multi-agent, GenAI, and Vision AI workflows — ensuring consistency, quality, and strategic alignment at scale.Drive backlog prioritization at the product-area level, balancing customer value, technical feasibility, AI accuracy expectations, model/vision constraints, and team capacityRun sprint planning, team stand-ups, and retrospectives; create the operating rhythm and working environment for engineers and data scientists to do their best workProactively engage and act as the bridge between the Product Management, business stakeholders and AI engineering — influencing product vision & feature prioritization (definition, scope, and sequencing) from deep understanding of technical possibility and commercial reality influence feature.Partner with Product Managers to shape feature roadmaps, bringing technical and AI-specific insight that meaningfully influences what gets built, when, and at what quality bar.Drive structured refinement sessions with the team, ensuring stories are technically complete and aligned on solution approach before development begins.Define and enforce quality standards for user story delivery — including extraction accuracy, edge-case coverage, agent behaviour expectations, and non-functional requirementsLead post-implementation validation efforts — coordinating UAT, output-quality reviews, production monitoring, and closing the loop with stakeholders on commercial outcomesSupport product activation and customer adoption — translating delivery milestones into customer-facing readiness for data/knowledge product rolloutDefine and champion organization-wide standards for user story authoring, solution design, backlog management, and delivery quality for AI-powered knowledge productsLead complex, cross-functional AI initiatives from discovery through delivery — managing dependencies, risks, and stakeholder expectations across teams.Coach and mentor team members, conduct performance conversations, and contribute to hiring decisions for the AI engineering and data science team.Establish frameworks for post-implementation validation, output-quality governance, production monitoring, and customer success during product activation and adoption at scaleIdentify systemic delivery bottlenecks and drive process improvements that raise velocity and quality across the product organizationBuild and mentor a high-performing team of AI agent engineers, and data scientists — driving hiring, onboarding, performance management, and career development at scaleShape organizational design, team structure, and operating model for the AI data engineering function as the business scalesRequired Skills & ExperienceTechnical SkillsMinimum 6-8 years of experience in AI Engineering Delivery Lead, or AI Program Lead, or Engineering Manager roles, with at-least 2-3 years operating as AI Engineering Principal within a SaaS, AI, or data-product organization.Deep expertise and Proven experience in AI solution design and technical scoping for AI-driven features — ideally including GenAI, LLM-based capabilities, Vision AI, and multi-agent workflows.Strong command of scaled Agile delivery, cross-team dependency management, and delivery governance frameworks, backlog management, sprint planning, and Agile delivery tooling at scale (Jira, Confluence, Miro, or equivalent)Ability to engage meaningfully with AI engineers and data scientists on architecture decisions, agent orchestration, prompt design, Vision AI trade-offs, and model behaviourFluency in the AI product development lifecycle — extraction accuracy, model evaluation, prompt engineering considerations, non-deterministic behaviour, and production monitoringSolid understanding of evaluation approaches for AI outputs — accuracy metrics, ground-truth validation, human-in-the-loop review, and output-quality benchmarkingFamiliarity with unstructured data extraction challenges across document, image,