About this role
About this role
We are building a next-generation, AI-native platform ecosystem for Alpha Generation that applies intelligent automation and agentic capabilities across investment technology and platform services, with a strong emphasis on reusable, platform-first capabilities over bespoke point solutions. This platform-first vision focuses on proactively monitoring, predicting, improving, and scaling engineering and operational workflows across core investment and technology pillars.
We are seeking a Vice President, AI Applied Engineer, a hands-on engineering leader who will shape and deliver this vision by driving AI‑first platform initiatives from concept through production at scale. This role will serve as a catalyst for adopting applied AI across the Alpha Generation platform organization, embedding modern engineering practices and accelerating platform maturity by enabling self-service, automation, and AI-powered workflows that reduce operational friction across teams. In this role, you will lead the design and delivery of a production‑grade and purpose‑built agentic AI capabilities.
This is an individual contributor leadership role, requiring deep technical ownership and strong influence. You will work closely with globally distributed engineering teams and partner with research, technology, and business leaders to drive platform‑wide development initiatives and foster an AI‑first mindset across the Alpha Generation ecosystem.
Key Responsibilities
Technical Strategy & Platform Delivery
- Define the technical direction and roadmap for applied AI and create AI agents to automate capabilities across the platform.
- Architect and scale multi-agent orchestration workflows (e.g., LangGraph or equivalent), ensuring stateful, production-grade reliability.
- Drive AI initiatives end-to-end, from prototyping through MVP to production, with a bias toward common building blocks, shared services, and patterns that can be reused across the Alpha Generation platform, spanning data pipelines, backend services, platform integrations, and user-facing interfaces using Agile delivery practices.
Evaluation, Governance & Engineering Excellence
- Design evaluation frameworks for agent behavior, decision quality, execution trajectories, and system performance, prioritizing reliability, transparency, and business relevance.
- Champion engineering best practices: automated testing, observability, CI/CD, prompt and model evaluation, and production safeguards.
- Establish responsible AI governance standards covering explainability, auditability, risk management, and regulatory compliance for a financial services context.
Stakeholder Engagement & Cross-Functional Impact
- Partner with product, engineering, data, and business teams to identify high-value use cases and translate requirements into scalable AI-driven solutions.
- Drive platform thinking with reusable agent patterns and shared capabilities that scale across domains, not bespoke one-offs.
Technical Qualifications
- Programming & Engineering: Strong command of Python and modern software engineering including production design patterns, CI/CD, automated testing, and observability for AI-driven systems.
- Agentic AI Systems: Demonstrated experience designing and deploying stateful, multi-step AI systems using agentic orchestration frameworks (e.g., LangGraph or equivalent).
- NLP Foundations: Solid grasp of core NLP concepts — tokenization, embeddings, semantic search, and information retrieval techniques.
- Data Science & Experimentation: Strong foundation in statistical modelling, data preprocessing, evaluation methodologies, and experimental design.
- RAG & Retrieval Systems: Deep understanding of RAG architectures, retrieval pipeline optimisation, and vector database design.
- ML Frameworks & Cloud Infrastructure: Hands-on experience with frameworks such as PyTorch or TensorFlow, and with cloud-native deployment environments including Kubernetes and containerisation.
- Enterprise Integration: Experience designing AI systems that interface with enterprise backend platforms, APIs, and large-scale data pipelines.
- Graph-Based Modelling: Familiarity with graph data structures and libraries (e.g., NetworkX, Neo4j) for modelling complex dependencies and relationships.
Skills & Experience
- Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, AI/ML, or a related quantitative discipline.
- 10+ years of progressive experience building and shipping engineering, AI, or ML systems end-to-end in production, with recent hands-on work delivering LLM-based applications or agentic workflows.
- 3+ years leading engineering teams or large-scale cross-functional technical programmes, with a proven track record of driving delivery and organisational impact.
- Demonstrated ability to manage complex technical programmes across multiple work streams, delivering high-quality outcomes at scale within Agile product and engineering environments.
- Excellent written and verbal communication skills, with the ability to influence and align senior technical and business stakeholders.
- Hands-on proficiency with prompt engineering, RAG pipelines, entity extraction, vector search, model evaluation, fine-tuning, and backend system integration.
- Active engagement with the open-source AI community and a consistent track record of staying current in the fast-moving generative AI ecosystem.
- Experience in financial services, asset management, or investment management is preferred.
This role requires strong technical leadership through influence - shaping direction, standards, and adoption across teams while remaining deeply hands-on in designing and delivering production-grade 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 (opens in new window) | Twitter: @blackrock (opens in new window) | LinkedIn: www.linkedin.com/company/blackrock (opens in new window)
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.
