VP, AI Data Engineering Lead

About this role

The 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.

• Lead 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 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 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 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

• Define and champion organization-wide standards for user story authoring, solution design, backlog management, and delivery quality for AI-powered knowledge products

• Lead 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 scale

• Identify systemic delivery bottlenecks and drive process improvements that raise velocity and quality across the product organization

• Build and mentor a high-performing team of AI agent engineers, and data scientists — driving hiring, onboarding, performance management, and career development at scale

• Shape organizational design, team structure, and operating model for the AI data engineering function as the business scales

Technical Skills

• 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 behaviour

• Fluency in the AI product development lifecycle — extraction accuracy, model evaluation, prompt engineering considerations, non-deterministic behaviour, and production monitoring

• 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 extraction challenges across document, image, and multimodal inputs.

• Advanced grasp of product analytics, AI output-quality measurement, and outcome-based evaluation frameworks for data/knowledge products

• Deep understanding of responsible AI principles (accuracy governance, data provenance, and user trust as they relate to commercialized AI outputs) and their practical implications for feature design, accuracy governance, customer trust, and regulatory considerations

Non-Technical & Interpersonal Skills

• Executive-level communication skills — able to operate fluidly between engineering stand-ups and boardroom strategy conversations.

• Excellent communication and stakeholder management skills — able to drive alignment across product, commercial, engineering, and domain-expert audiences

• Strong strategic thinking — able to connect present engineering decisions & strategy to long-term commercial positioning of AI knowledge/data products.

• Strong analytical and structured problem-solving approach — breaks down complex extraction and knowledge-structuring problems into clear, actionable paths forward

• Emotional intelligence and people-first leadership style — able to inspire, coach, and hold a diverse team of engineers and scientists to a high bar; earns trust quickly across diverse stakeholder groups including customers, commercial, and technical teams.

• Business acumen — understands the fundamentals of financials services industry and/or software/data product business, and how product output quality & timeliness directly affects customer trust and revenue.

Leadership & Ownership

• Proven track record of building, leading, and developing teams of engineers, data scientists, or technical specialists in an AI or data product context at scale.

• Demonstrable experience influencing product vision and feature strategy at the leadership level — shaping what gets built, not just how.

• Experience establishing organization-wide standards, frameworks, and practices that persist beyond individual initiatives.

• Proven ability to set technical direction, manage delivery, and drive accountability across a cross-functional team.

• Track record of mentoring individuals, running performance conversations, and contributing to hiring and team-building.

• Courage to push back on feature scope or timelines when extraction accuracy, reliability, or commercial viability — or team sustainability — are at risk

• Proven track record of owning complex AI delivery outcomes end-to-end, including post-launch validation and adoption.

• Hands-on experience with hiring, team design, performance management, and succession planning for technical AI teams.

• Ability to hold the bar on quality and delivery while advocating for the team — protecting focus, escalating constraints, and making hard prioritization calls

• A builder-of-builders mindset — measures success by the capability and culture left behind, not just the features shipped.

• Direct leadership of a high performing team of AI agent engineers and data scientists working on commercially impactful AI systems & knowledge product portfolio — with meaningful autonomy and ownership.

• Direct influence over organizational direction — working closely with executive leadership on product vision, hiring, and operating model decisions.

• A meaningful seat at the table in shaping product & engineering strategy, feature prioritization, and delivery practices for commercialized AI capabilities

• A platform to build and lead a world-class organization of AI engineers, data scientists, and Product Leads — creating lasting capability in commercialized AI.

• Direct exposure to emerging AI capabilities — multi-agent orchestration, GenAI, Vision AI — applied to real commercial problems at scale.

• A clear path toward head-of-Engineering function leadership roles, with investment in mentorship, external learning, and executive development.

Our benefits
To help you stay energized, engaged and inspired, we offer a wide range of benefits including a strong retirement plan, tuition reimbursement, comprehensive healthcare, support for working parents and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.

Our hybrid work model

BlackRock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.

About BlackRock

At BlackRock, we are all connected by one mission: to help more and more people experience financial well-being.  Our clients, and the people they serve, are saving for retirement, paying for their children’s educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.

This mission would not be possible without our smartest investment – the one we make in our employees. It’s why we’re dedicated to creating an environment where our colleagues feel welcomed, valued and supported with networks, benefits and development opportunities to help them thrive.

For additional information on BlackRock, please visit @blackrock | Twitter: @blackrock | LinkedIn: www.linkedin.com/company/blackrock

BlackRock is proud to be an Equal Opportunity Employer.  We evaluate qualified applicants without regard to age, disability, family status, gender identity, race, religion, sex, sexual orientation and other protected attributes at law.