Director, Software Engineering- Machine Learning (Individual Contributor)

Fidelity • Boston, MA

10d

About The Position

Fidelity’s Asset Management Technology organization is hiring a senior individual contributor for the Graph Technology team within Data Science. The role is comparable to a Principal Engineer at many firms and is intended for a hands-on engineer who sets technical direction and leads through influence rather than people management.

The Director will serve as technical lead for production AI applications—including large language model systems, retrieval systems, and knowledge graphs—that support investment decisions by quants and portfolio managers. The role owns the end-to-end architecture of multiple ML applications, establishes engineering standards, and remains actively involved in coding. Work involves close collaboration with investment professionals to turn complex research questions into reliable, scalable tools, as well as partnering with data engineering, platform, risk, and compliance teams to deliver well-governed AI in a regulated environment.

The Graph Technology team is part of Quant Technology within Fidelity’s Asset Management Technology organization. It builds scalable investment tools, including knowledge graphs connecting companies, securities, and events and LLM-powered research tools. Its work supports Asset Management’s investment performance, scale, and efficiency.

Fidelity is transitioning to a full-time onsite working model through a phased rollout across regions and roles. Onsite expectations vary by role and location and may evolve; the transition does not apply to fully remote roles. Fidelity will not provide immigration sponsorship for this position. The base salary range is $126,000–$255,000 USD per year. Placement depends on responsibilities and scope, geographic location, relevant experience, and other factors. Compensation may also include bonus or other variable compensation, depending on position and eligibility. The application window closes when the position is filled or unposted.

Requirements

• At least 8 years of software or machine learning engineering experience, with a track record of leading technical design for ML products shipped to production.
• Expert Python skills and strong SQL skills, including 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, such as Neo4j and Cypher.
• Strong DevOps experience with CI/CD, such as Jenkins or GitHub Actions; containers and orchestration, such as Docker and Kubernetes; and observability, including logging, metrics, and alerting.
• Experience with a major cloud platform (AWS, Azure, or GCP) and its ML services.
• Experience building data pipelines and orchestrating workflows, such as with Airflow.
• Strong software engineering practices, including testing, code review, maintainable design, and agile delivery.
• Technical influence beyond one’s own team, such as mentoring, setting standards, or driving architecture decisions.
• Ability to explain complex models, results, and trade-offs clearly to non-technical and investment audiences.

Nice To Haves

• Deep MLOps experience across the production ML lifecycle, including model and LLM evaluation, performance tuning, model and prompt versioning, and monitoring quality, drift, latency, and cost.
• Experience with investment data, such as market data, fundamentals, holdings, or alternative data, or with buy-side workflows.
• Experience with LLM guardrails, hallucination reduction, model risk management, or AI governance.
• Background in graph ML, such as graph neural networks, entity resolution, or ontology design.
• Experience with enterprise job schedulers, such as Autosys.
• Familiarity with front-end tools for rapid prototyping, such as Streamlit, React, or Vue.
• Advanced degree in Computer Science, Software Engineering, or a related field; equivalent experience is equally valued.

Responsibilities

• 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, including data pipelines, model serving, evaluation, and production monitoring.
• Set engineering standards for AI evaluation, MLOps practices, and code quality, and drive adoption across the team.
• Work directly with quants and portfolio managers to define problems, set priorities, and shape the product roadmap, including decisions about what to build and how.
• Mentor engineers at every level through design reviews, code reviews, and technical guidance.
• Collaborate across data engineering, platform, risk, and compliance teams to deliver well-governed AI in a regulated environment.
• Evaluate new AI technologies and vendors and make build-versus-buy recommendations on LLM providers, graph platforms, and tools.
• Continue hands-on software development and coding while leading technical direction.

Benefits

• Comprehensive health care coverage.
• Emotional well-being support.
• Retirement benefits.
• Generous paid time off.
• Parental leave.
• Charitable giving employee match program.
• Educational assistance, including student loan repayment, tuition reimbursement, and learning resources.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed