VP, AI Data Engineering Lead

About this role The VP AI Data Engineering Lead brings sharp judgment, broad ownership, and direct team leadership to the development of production-grade AI systems at the core of a data and knowledge product business. He/she lead a cross-functional team of AI agent engineers and data scientists — setting technical direction, driving delivery, and ensuring the multi-agent GenAI Vision AI workflows they build meet the accuracy, scalability, and commercial quality bar the business depends on. In parallel, the VP Product Lead shapes solution design upstream, drives backlog decisions with strategic intent, and influences how features are defined long before they enter a sprint. He/she work in close partnership with the Product Manager, domain experts, and commercial stakeholders — bringing a technically grounded perspective that shapes product vision, feature scope, and prioritization for the knowledge products being commercialized. The VP Product Lead has navigated the inherent complexity of AI product delivery — model accuracy, non-deterministic behaviour, vision-model edge cases, data dependencies — and knows how to keep quality and velocity in balance while developing the people doing the work. This is a role for someone who leads from the front, builds high-performing teams, and holds themselves accountable for the commercial credibility of what ships. Roles & Responsibilities Lead a team of AI agent engineers and data scientists — setting technical direction, managing delivery, driving performance, and developing individual capability across the team Own end-to-end solution design for complex AI features — bridging the PM’s feature intent and the engineering team’s technical approach for multi-agent, GenAI, and Vision AI workflows Drive backlog prioritization at the product-area level, balancing customer value, technical feasibility, AI accuracy expectations, model/vision constraints, and team capacity Run sprint planning, team stand-ups, and retrospectives; create the operating rhythm and working environment for engineers and data scientists to do their best work Proactively engage with the Product Manager and business stakeholders to influence feature definition, scope, and sequencing — particularly where extraction accuracy, pipeline reliability, or commercial viability are at stake 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 requirements Lead post-implementation validation efforts — coordinating UAT, output-quality reviews, production monitoring, and closing the loop with stakeholders on commercial outcomes Support product activation and customer adoption — translating delivery milestones into customer-facing readiness for data/knowledge product rollout Coach and mentor team members, conduct performance conversations, and contribute to hiring decisions for the AI engineering and data science team Required Skills & Experience Technical Skills 5–8 years of experience in AI Engineering Delivery, or AI Program Lead, roles within a SaaS, AI, or data-product organization 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 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 behaviour Solid understanding of evaluation approaches for AI outputs — accuracy metrics, ground-truth validation, human-in-the-loop review, and output-quality benchmarking Familiarity with unstructured data extrac ... (truncated, view full listing at source)