Team Lead- GenAI Technology

Job Description:

GenAI Team Lead – Asset Management Technology 

The Role 

You are an experienced engineering lead 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 help lead an applied technical group conceiving of, building and delivering first-of-a-kind knowledge-based applications for investment professionals. You have prior experience managing a diverse group of talented individual contributors (with expertise in UI/UX, full-stack system development and AI) to deliver production applications using rapidly evolving, powerful yet brittle AI technology. You are comfortable working in a quick time to market technical area where the lag between conceptualization, research and development, and productionization is optimized. You are skilled at working with senior business, architecture and engineering stake-holders, mentoring junior individual contributors, helping them grow and learn new skills in today’s fast-paced AI field. You possess excellent leadership and management skills and revel in working as part of a team to solve deep applied problems. 

You will have: 

· A PhD or Master's in AI/ML, Computer Science or Finance (with a background in Machine learning), with ten years plus 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.  

· 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. 

· 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 at 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:

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.