AI Product Solutions Lead, Technology Product Management, Vice President

BlackRock • New York, NY

2d • $162,500 - $215,000 • Hybrid

About The Position

The AI Product Solutions Lead will lead the integration and channel experiences workstream for a single AI-powered platform, developing and evolving the product from concept through launch, adoption, and ongoing optimization. The role defines product vision and strategy for the workstream, translates complex business and user needs into technical product requirements, prioritizes the roadmap, and partners with engineering, data science, architecture, design, business teams, and client businesses to deliver scalable AI solutions.

With a primary focus on integration and channel experiences, the role connects the AI product to the applications, workflows, and touchpoints through which users engage with the platform. The position combines traditional product management with a product solutions architecture perspective, requiring a strong understanding of data, models, APIs, AI agents, retrieval and grounding approaches, integrations, channels, and platform architecture. This technical depth supports solution design, architectural trade-off evaluation, and informed product decisions in partnership with engineering and architecture teams.

The role defines success measures, evaluates AI quality and user experience, monitors product performance, incorporates user feedback, and drives continuous improvement as models, data, technology, and user needs evolve. BlackRock’s hybrid model requires employees to work in the office at least four days per week, with the flexibility to work from home one day per week; some groups may require more office time.

Requirements

• Experience leading technical product management, including product strategy, roadmap development, prioritization, technical requirements definition, and ownership of products from concept through production launch and adoption.
• Knowledge of AI and machine learning product development, including generative AI, large language models, AI agents and agentic workflows, prompting, context management, and model capabilities.
• Working knowledge of retrieval-augmented generation (RAG), grounding, knowledge retrieval, semantic data models, and structured and unstructured data approaches.
• Experience partnering on product solution and platform architecture, including APIs, system integrations, data pipelines, and technical trade-offs; ability to shape technical solutions without being the primary engineer implementing them.
• Experience defining product and AI quality measures and using evaluation, telemetry, experimentation, quantitative insights, and qualitative user feedback to improve product experiences and outcomes.
• Understanding of responsible AI principles and considerations related to security, privacy, governance, evaluation, monitoring, and controls for production AI products.
• Ability to translate complex business workflows and user needs into scalable product and technical solutions and communicate effectively across engineering, data science, architecture, design, business teams, and senior stakeholders.

Responsibilities

• Define AI product vision and strategy and own the end-to-end product lifecycle, from problem discovery and requirements through development, launch, adoption, measurement, and continuous optimization.
• Translate business and user needs into technical product requirements and own the roadmap and backlog, balancing user value, business impact, technical feasibility, data readiness, model performance, risk, and engineering effort.
• Partner across engineering, data science, architecture, design, and business teams to shape scalable solutions across data requirements, integrations, APIs, grounding approaches, orchestration, and platform capabilities.
• Shape AI-powered and agentic experiences, including conversational interfaces, intelligent recommendations, automated workflows, decision support, and human-in-the-loop experiences; drive experimentation across models, context, grounding, workflows, and user experiences.
• Define AI evaluation frameworks and product KPIs, using telemetry, experimentation, user feedback, and production performance to improve quality, relevance, reliability, scalability, adoption, productivity, and business outcomes.
• Define product requirements for responsible AI, including transparency, explainability, access controls, data privacy, evaluation, monitoring, security, governance, and appropriate human oversight.
• Evaluate emerging AI capabilities and translate relevant advances into practical product opportunities grounded in user needs, product strategy, architectural considerations, and measurable business value.
• Apply sound judgment and critical thinking to solve complex problems, adapt as the business evolves, explore new approaches and technologies, and rigorously challenge results.

Benefits

• Annual discretionary bonus
• Healthcare and comprehensive healthcare
• Leave benefits
• Retirement benefits and a strong retirement plan
• Tuition reimbursement
• Support for working parents
• Flexible Time Off (FTO)
• Hybrid work model with flexibility to work from home one day per week

What This Job Offers

• Job Type: Full-time
• Career Level: Manager
• Education Level: No Education Listed