Director, Software Engineering- Machine Learning (Individual Contributor)

About the role

Fidelity will not provide immigration sponsorship for this position.

Job Description:

The Role

Asset Management Technology is hiring a Director, Software Engineering – Machine Learning for its Graph Technology team within Data Science. This is a senior individual contributor role, comparable to Principal Engineer at many firms. It's for a hands-on engineer who wants to set technical direction and lead through influence rather than people management.

You'll be the technical lead for production AI applications, including large language model (LLM) systems, retrieval systems and knowledge graphs, used by quants and portfolio managers to make investment decisions. You'll own the architecture of these systems from start to finish, set engineering standards for the team, and keep writing code. Most of your work will be done directly with investment professionals, turning complex research questions into reliable, scalable tools.

- Design and lead development of production LLM, retrieval and knowledge-graph systems, for investment research and decision support

- Own the end-to-end architecture of multiple ML applications: data pipelines, model serving, evaluation and production monitoring

- Set engineering standards for AI evaluation, MLOps practices and code quality, and drive their adoption across the team

- Work directly with quants and portfolio managers to define problems, decide priorities and shape the product roadmap, deciding what to build as well as how

- Mentor engineers at every level and raise the bar through design reviews, code reviews and technical guidance

- Work across teams, including data engineering, platform, risk and compliance, to deliver well-governed AI in a regulated environment

- Evaluate new AI technologies and vendors and make build-vs-buy recommendations on LLM providers, graph platforms and tools

The Expertise and Skills You Bring

Required

- 8+ years in software or machine learning engineering, with a track record of leading the technical design of ML products shipped to production

- Expert Python and strong SQL, with experience working with large-scale databases

- Strong experience building scalable backend services and APIs to serve ML models

- Hands-on production experience with LLMs and generative AI, information retrieval and vector databases

- Practical experience with graph databases and knowledge-graph modeling (e.g., Neo4j, Cypher)

- DevOps: Strong experience with CI/CD (e.g., Jenkins, GitHub Actions), containers and orchestration (Docker, Kubernetes), and observability (logging, metrics, alerting)

- Experience with a major cloud platform (AWS, Azure or GCP) and its ML services

- Experience building data pipelines and orchestrating workflows (e.g., Airflow)

- Strong software engineering practices: testing, code review, maintainable design and agile delivery