About this role
Responsible for preparing the 'big data" infrastructure to be analyzed by Data Scientists and Analysts. Designs, builds, integrates, tests and maintains highly scalable data management systems with data from various sources and then writes complex queries to ensure data is easily accessible and analytics work smoothly. May employ a variety of languages and tools to integrate new data management technologies and software engineering tools into existing structures. May work closely with others to determine what data management systems are appropriate and is responsible for problem-solving database integration issues and handling messy, unstructured data sets. The overall goal is optimizing the performance of the firm's big data ecosystem. Key Responsibilities Define and drive the technical strategy and roadmap for applied AI capabilities within Hyperion, aligning AI investments with business priorities across Private Markets. Lead the architecture, design, and scaling of complex multi-agent workflows using frameworks such as LangGraph or similar orchestration frameworks. Drive the end-to-end lifecycle of AI applications, from proof of concept to MVP to production deployment, including data pipelines, backend services, and platform integration, leveraging Agile methodologies to deliver iteratively and at scale. Lead the integration of LLM-powered capabilities into core business products and platform services, ensuring high availability, low latency, resilience, and maintainability. Establish robust evaluation frameworks to assess agent behavior, trajectories, decision quality, and system performance, with a strong focus on reliability, explainability, and business relevance. Define and enforce engineering best practices for applied AI, including testing, observability, CI/CD, model and prompt evaluation, and production controls. Establish standards for responsible AI, governance, risk management, and operational excellence appropriate for a regulated enterprise environment. Partner closely with product, engineering, data, and business stakeholders to prioritize use cases, translate business needs into scalable AI-enabled solutions, and drive adoption across the platform. Build, mentor, and lead a high-performing team of engineers and applied AI practitioners, fostering technical excellence, collaboration, and continuous learning. Drive the development of reusable platform capabilities and patterns that enable scalable adoption of AI across products and workflows, rather than one-off implementations. Stay current with developments in the generative AI landscape and evaluate emerging tools, models, and frameworks for their practical application within BlackRock. Technical Qualifications Strong proficiency in Python and modern software engineering practices, including production design patterns, CI/CD, automated testing, and observability for AI systems. Proven experience building and deploying stateful, multi-step AI systems using agentic orchestration frameworks such as LangGraph. Strong understanding of core NLP concepts, including tokenization, embeddings, semantic search, and information retrieval. Solid foundation in data science and experimentation, including statistical modeling, data preprocessing, evaluation methodologies, and experimental design. Experience working with graph-based data structures and libraries such as NetworkX to model complex relationships, workflows, and dependencies. Deep understanding of RAG architectures, retrieval pipelines, and vector databases. Familiarity with Transformer architectures and approaches to fine-tuning, adapting, or evaluating language models. Experience with AI/ML frameworks such as PyTorch or TensorFlow, and with cloud-native deployment environments including Enterprise-grade container orchestration platform supporting declarative infrastructure and horizontal scaling. Experience designing and integrating AI systems with enterprise backend platforms, APIs, and data pipelines. Skills and Experience Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, AI/ML, or a related quantitative field. 15+ years of experience building and deploying engineering, AI, or ML systems end to end, including recent experience delivering LLM-based applications or workflows in production. 3+ years of experience leading teams or large-scale cross-functional initiatives, with a track record of driving technical delivery and organizational impact. Demonstrated success leading complex technical programs, managing multiple priorities, and delivering high-quality solutions at scale within Agile product and engineering environments. Strong written and verbal communication skills, with the ability to influence senior technical and business stakeholders. Hands-on experience with prompt engineering, RAG pipelines, entity extraction, embeddings/vector search, model evaluation, fine-tuning, and backend integration. Strong interest in open-source language models and a track record of staying current with developments in the rapidly evolving generative AI ecosystem. Experience in financial services, asset management, or private markets is preferred. Understanding of the private markets investment lifecycle and data landscape is a plus. Preferred Leadership Profile Strategic thinker with the ability to translate business priorities into scalable technical solutions. Hands-on leader who can operate effectively across strategy, architecture, and execution. Strong people leader with experience mentoring senior engineers and building high-performing teams. Comfortable operating in a fast-moving environment with evolving priorities and emerging technologies. Passionate about building scalable platforms and reusable capabilities rather than isolated prototypes. Committed to engineering rigor, responsible AI practices, and measurable business outcomes.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.
Guidance on AI use for candidates
At BlackRock, AI has long been part of how we work – enhancing decision-making, improving operations, and helping us deliver better outcomes for clients. We encourage candidates to use AI thoughtfully to learn, prepare, and work more effectively; but during our interview process, we want to focus on getting to know you through your own experiences, thinking, and judgment. To support you, we’ve provided guidance on when and how to use AI during our hiring process so you can approach each step with confidence and showcase your best self.
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.
To learn more about BlackRock, please visit Careers.BlackRock.com. We also encourage you to get to know us on LinkedIn, Instagram, YouTube, X, and TikTok.
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.
