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BlackRock
VP ,Data Engineering
BlackRock
Mumbai•Hybrid•8-11 Yrs•Jul 2
Job description
Hybrid
Overview
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.
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
Qualifications
- 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
- 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.
- 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.
- 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.