Principal GenAI Engineer (Asset Management Technology)

Job Description : The Role

You are a seasoned scientist with a deep understanding of GenAI, and a burning desire to unlock its mysteries in the context of a professional domain such as finance. You are excited about this once-in-a-lifetime opportunity to completely rethink the foundations of your discipline (Computer Science, AI, Finance) around an epoch-defining new technology (in this case, GenAI). You are at home in an applied technical group conceiving of, building and delivering first-of-a-kind knowledge-based applications. You have excellent collaboration skills and revel in working as part of a team to solve deep applied problems.

You will have :

• You have a PhD or Master's in AI / ML, Computer Science or Finance (with a background in Machine learning), with 7 plus years of industrial experience.
• An understanding of how state-of-the-art LLMs can be induced to use explicitly supplied structured and unstructured information, their implicit world knowledge and their slow-thinking abilities to solve domain-specific problems (such as extraction, evaluation and comparison or argument structure in investing domains).
• A grasp of logic- and ML-based AI techniques for knowledge extraction, representation and reasoning. Expertise in combining symbolic, logic-based representations (e.g. knowledge graphs, ontologies) with informal, text-based domain content to solve business problems
• An understanding of how to deliver into production GenAI applications that can reliably process information in open-ended settings, e.g. by playing off state-of-the-art LLMs (from OpenAI, Google, Anthropic) against each other.
• A track record of managing the "exploration vs exploitation" tradeoff to deliver innovative solutions for first-of-a-kind problems.
• Deep expertise in Python, data-centric AI (ML) techniques, particularly as applied to text, tables, documents. Excellent knowledge of standard tools of the trade for machine learning engineers.

The Team

We are developing a Large Language Model (LLM)-first knowledge stack for investment professionals in Fidelity Asset Management - analysts and portfolio manager in equities, fixed income, high income, direct lending. The stack will be able to process all the documents of interest to analysts - e.g., analyst reports, earnings notes, spreadsheet models, prospectuses, loan indentures, news reports, regulatory filings. It will support the deployment of personalized assistants that can assist principals in their full range of information consumption / processing / production tasks.

Certifications : Category :

Data Analytics and Insights

Fidelity's hybrid working model blends the best of both onsite and offsite work experiences. Working onsite is important for our business strategy and our culture. We also value the benefits that working offsite offers associates. Most hybrid roles require associates to work onsite every other week (all business days, M-F) in a Fidelity office.