BlackRock
Artifical Intelligence Engineer, Portfolio Management Group, Vice President
About this role
Team Overview
Data Strategy & Solutions is a Global Research & Data team within BlackRock Portfolio Management Group (PMG) focused on developing AI powered products and generating actionable insights using alternative, primary and big data. We are seeking an experienced Data Scientist, with experience in Large Language Models, Natural Language Processing, (NLP), and Transformer Models, to spearhead the build-out of AI powered capabilities within Investment Research, Product Strategy, and Process Optimization.
There are four groups within Data Strategy & Solutions – Investment AI, Insights & Data Products, Solutions, and Platform. All these groups work towards various aspects of Alpha Generation using Alternative data for a host of portfolio management functions.
As a member of Investment AI, you will be working with Investment Researchers, and Portfolio Managers to build capabilities that enable alpha generation and streamlining research processes using Generative AI and Natural Language understanding techniques.
Role Overview
The AI Lead is an investor-facing technical partner with deep AI, data, and systems expertise who designs and builds AI-powered research applications that integrate seamlessly into investor research workflows.
AI Leads sit within research pods and collaborate directly with investors across Portfolio Management Group (PMG) to understand their business, map research workflows, identify opportunities for AI leverage, and translate those into intuitive, scalable research applications.
They own the technical vision, design, architecture, and product experience of specific research apps, partnering with Research Engineers, Engineering Hub and Platform to ensure these applications are robust, usable, and aligned with investor objectives.
Key Responsibilities
AI System Design & Architecture
Lead architecture and design of pod-level AI-powered research applications.
Build and optimize LLM-based features, retrieval flows (RAG), agent workflows, and search capabilities.
Leverage enterprise platform capabilities (data engines, data model services, orchestration, and compute infrastructure) to deliver scalable AI systems.
Collaborate with Engineering Hub and Platform to ensure solutions are production-ready, secure, observable, and aligned with engineering best practices.
Investor-Facing Research Partnership
Serve as the technical partner to investors - deeply understanding their business needs, research workflows, and analytical challenges.
Identify opportunities to embed AI, automation, and advanced analytics into investor workflows.
Translate open-ended research questions into clear, technically feasible product concepts.
Present prototypes, demos, and insights to investors for feedback and iteration.
Application Development & Product Experience
Own the end-to-end user experience and functionality of the investor pod’s research apps.
Partner with Full Stack Engineers to design high-quality, intuitive UIs for investment workflows.
Guide Research Engineers on data preparation and structuring for AI apps.
Collaborate on workflow integration, ensuring apps fit naturally into investor research processes.
AI Capability Development
Drive the lifecycle from POC to MVP to production-grade AI applications.
Evaluate new AI models, APIs, and frameworks for potential research impact.
Prototype emerging AI capabilities (summarization, reasoning, search, classification, agent tooling).
Establish best practices for prompt design, evaluation, safety, reliability, and model testing.
Partner with internal and external technology teams to leverage state of the art AI tools to serve investor requirements.
Cross-Functional Collaboration
Partner with Ops Intelligence to embed monitoring, observability, and data-quality safeguards into AI apps.
Work with Workflow Automation Engineers to integrate internal tools, platform capabilities, and reusable components.
Contribute to architecture reviews and cross-pod technical patterns.
Collaborate with Research Engineers to align data workflows and AI requirements.
Collaborate with Platform to enhance and improve existing capabilities or add new capabilities building upon insights from client interactions.
Qualifications
Required:
Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or equivalent.
5+ years of work experience delivering ML, AI, and data-intensive systems.
Hands-on experience building and deploying AI systems end-to-end - including LLM workflows, prompt engineering, RAG pipelines, embeddings/vector search, fine-tuning, evaluation, and backend integration using Python and SQL.
Strong written and oral communication skills and ability to work directly with investors and senior partners.
Preferred:
Domain specific experience is a plus - building data-driven / analytical applications for investment research, investment management or financial services.
Hands-on experience with cloud platforms (AWS/GCP/Azure/Snowflake) and enterprise AI infrastructure, including model deployment and monitoring.
Having worked with open-source language models and keen interest in staying current with developments in the rapidly evolving generative AI landscape.
Exposure to multi-modal models and applications.
Proficiency in front-end or full-stack development patterns for research apps.
Key Attributes
Deep curiosity about investment management and investment research.
Ability to translate business and research needs into technical solutions.
Technical leadership from concept to implementation.
Strong product intuition and user-centric thinking.
Ability to simplify complex AI systems into intuitive user experiences.
High standards for quality, reliability, and safety in AI systems.
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.
