Principal Quant Developer (MLOps)

We are part of Fidelitys Asset Management Technology organization, within the Quantitative Research & Investing Technology team. Our group collaborates closely with Advance Strategies and Research on sophisticated initiatives such as systematic investment strategies, portfolio construction, risk management, alpha research, and GenAI. We build high-quality, highly scalable solutions that enhance our asset management efficiency and decision-making. The base salary range for this role is $107,000 to $216,000 USD annually, with placement depending on responsibilities, scope, location, and experience. In addition to base pay, the total compensation package may include bonus or other variable pay. We also offer a broad benefits package that includes comprehensive health coverage, emotional well-being support, retirement benefits, paid time off, parental leave, charitable matching, and educational assistance such as tuition reimbursement and student loan repayment.

- We require a bachelors degree in Computer Science, Financial Engineering, Information Technology, Information Systems, Mathematics, Physics, Statistics, Engineering, or a closely related discipline, along with six or more years of experience in a senior quantitative development or comparable position.
- Alternatively, we consider a masters degree or equivalent foreign education in one of the same fields, plus four or more years of experience in a lead quantitative development or similar role.
- We expect hands-on experience building reliable, high-performing systems that support financial investment decisions using R, Python, PL/SQL databases, and quantitative methods.
- We need deep Python expertise across the full development stack, with exposure to object-oriented programming and design patterns.
- We look for experience with unit testing frameworks and a solid understanding of test-driven development principles.
- We value a strong commitment to producing clean, maintainable, and efficient code.
- We require proficiency with SQL databases such as Oracle and Snowflake, and familiarity with NoSQL and graph databases.
- We expect experience with batch scheduling tools and API development, including Autosys, Airflow, FastAPI, and Flask.
- We look for proven capability in designing resilient data pipelines and event-driven workflows.
- We require system design and cloud architecture experience on AWS, including Lambda, S3, EKS, and EC2.
- We value experience with Docker, Kubernetes, Jenkins, Linux, GitHub, and infrastructure-as-code practices.
- We prefer experience operationalizing machine learning models on AWS, including SageMaker and Bedrock, as well as familiarity with MLflow.
- We seek knowledge of mathematics, statistics, quantitative finance, probability, linear regression, and time-series analysis.
- We value experience applying quantitative techniques to systematic investing, including forecasting, portfolio construction, risk management, alpha research, and simulation-based algorithms.
- We prefer domain knowledge in equities, fixed income, or alternative assets.
- Progress toward the CFA designation or an equivalent credential is a plus.
- We need strong presentation and communication skills, along with the ability to collaborate effectively with researchers and investment professionals.
- We expect strong problem-solving ability, creativity, intellectual curiosity, and a willingness to adopt new tools and best practices quickly.

- We partner with quantitative researchers to prototype and deliver new systematic investment strategies.
- We develop high-impact solutions across alpha research, portfolio construction, risk management, and related investment projects.
- We design and implement scalable, resilient, and efficient analytical and software solutions that improve investment workflows.
- We lead research initiatives through the full software development lifecycle using a full-stack approach.
- We help research teams create new models and products that strengthen our market advantage.
- We build and maintain robust data pipelines, APIs, and event-driven systems.
- We architect and support cloud-based solutions on AWS and contribute to infrastructure modernization.
- We create and manage CI/CD pipelines, containerized services, and infrastructure-as-code implementations.
- We operationalize machine learning and AI models in production, supporting MLOps and LLMOps practices.
- We apply quantitative and statistical techniques to develop forecasting, portfolio, and risk management tools.
- We collaborate across functions to translate research ideas into production-ready capabilities.
- We keep up with emerging technologies, advanced methods, and industry developments in quantitative finance and AI.