AI Data Engineering Lead, Vice President

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.

Roles & Responsibilities

• 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

Required Skills & Experience

Technical Skills

• Minimum 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 >

  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.

  
  Guidance on AI use for candidates

  
  

  At BlackRock, AI has long been part of how we work – enhancing decision-making, improving operations, and helping us deliver better outcomes for clients. We encourage candidates to use AI thoughtfully to learn, prepare, and work more effectively; but during our interview process, we want to focus on getting to know you through your own experiences, thinking, and judgment. To support you, we’ve provided guidance on when and how to use AI during our hiring process so you can approach each step with confidence and showcase your best self.
  

  
  

  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.

  
  

  To learn more about BlackRock, please visit Careers.BlackRock.com. We also encourage you to get to know us on LinkedIn, Instagram, YouTube, X, and TikTok.

  
  

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