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BlackRock
BlackRock

Multinational investment company.

VP, AI Data Engineering Lead

职能数据工程
级别VP级
地点Bengaluru, India
方式现场办公
类型全职
发布1周前
立即申请

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 & ExperienceTechnical 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 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

Black Rock’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 Black Rock.

About Black Rock

At Black Rock, 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

Black Rock 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.

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关于BlackRock

BlackRock

BlackRock

Public

Multinational investment company.

10,001+

员工数

New York City

总部位置

$114B

企业估值

评价

10条评价

3.8

10条评价

工作生活平衡

3.2

薪酬

4.1

企业文化

3.4

职业发展

3.7

管理层

2.8

72%

推荐率

优点

Good compensation and benefits

Learning and growth opportunities

Supportive team and collaborative culture

缺点

Long hours and demanding work culture

High expectations and stress

Management issues and disorganization

薪资范围

4,690个数据点

Junior/L3

L2

L6

L3

L4

L5

Junior/L3 · Analyst

1,924份报告

$118,963

年薪总额

基本工资

$100,050

股票

-

奖金

$18,913

$81,954

$175,627

面试评价

6条评价

难度

3.3

/ 5

时长

14-28周

录用率

17%

面试流程

1

HireVue

2

Online Assessment

3

Final Round/Superday

常见问题

Technical interviews

Behavioral questions

Role-specific assessments