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

Technical Skills

Required Skills & Experience

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 extraction challenges across document, image, and multimodal inputs
Working knowledge of responsible AI principles — accuracy governance, data provenance, and user trust as they relate to commercialized AI outputs

Non-Technical & Interpersonal Skills

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

Demonstrated experience directly leading and developing teams of engineers, data scientists, or technical specialists in an AI or data product context
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

What This Role Offers

Direct leadership of a team of AI agent engineers and data scientists working on commercially impactful AI systems — with meaningful autonomy and ownership
A meaningful seat at the table in shaping product strategy, feature prioritization, and delivery practices for commercialized AI capabilities
Direct exposure to emerging AI capabilities — multi-agent orchestration, GenAI, Vision AI — applied to real commercial problems at scale
A clear path toward principal and head-of-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.