Artificial Intelligence Engineer, Portfolio Management Group, Associate

Business Overview 

PMGTech is the horizontal technology platform within BlackRock’s Portfolio Management Group (PMG), integrating investment research with advanced engineering, AI, and alternative data to make technology a direct driver of alpha. It operates as one global team across three verticals – Platform Strategy, Research Solutions (DS&S), and Platform Change (IPT) – partnering closely with the BlackRock Aladdin ecosystem and PMG investment leadership. This integrated model delivers a coherent tech strategy, strengthens the research community, and scales capabilities across regions, asset classes (Equities, Fixed Income, Multi-asset), and investment styles (discretionary and systematic). 

PMGTech leads PMG’s AI strategy by building an AI-ready research environment, unified data layer, agentic orchestration networks, and an investment-focused application layer to deploy AI-powered research solutions into production. As part of PMGTech, you will work directly with investment researchers and portfolio managers to build and scale capabilities in data engineering, GenAI, and platform tooling, streamlining research workflows and enabling differentiated, alpha-generative investment insights across PMG’s investment pillars. 

Job Purpose / Background 

DS&S envisions the AI Lead Engineer role as an investor-facing technical partner role with deep AI, data, and systems expertise who designs and builds AI-powered research applications that integrate seamlessly into investor research workflows. 

AI Lead Engineers will sit within DS&S pods - asset-class focused sub-teams - and collaborate directly with investors across Portfolio Management Group (PMG) to understand business requirements, map research workflows, identify opportunities for AI leverage, and translate them into intuitive, scalable, production–grade research applications. 

AI Lead Engineers will 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 

• Design architectures for AI-powered research applications leveraging Generative AI capabilities (RAG, agentic workflows, search, model fine-tuning). 

• Partner with investors to translate open-ended research questions into feasible AI-driven product concepts. 

• Be hands-on in leading the lifecycle from POC to MVP to production for AI applications, including data pipelines and backend integration. 

• Own the end-to-end user experience of investor-facing research apps, including intuitive front-end UI designs. 

• Evaluate emerging models and APIs; define best practices for prompts, safety, reliability, and testing with internal and external tech teams. 

• Leverage enterprise data engines, orchestration frameworks, and secure/observable production practices with Engineering Hub and Platforms. 

• Implement monitoring, observability, evaluation frameworks, and data-quality safeguards for GenAI-powered research applications. 

Skills & Qualifications 

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or equivalent. 

• 3+ 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, entity extraction, embeddings/vector search, text2sql, 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 is a must. 

• Domain specific experience is a plus - building data-driven / research applications for investment research, investment management or financial services. 

• Hands-on experience with any major cloud platform, proficiency in front-end or full-stack development is a plus. 

• Preference for prior experience with open-source language models and actively staying current with developments in the rapidly evolving generative AI landscape. 

• Understanding of investment strategies and asset classes is a plus. 

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.