Principal Quant Developer (MLOps)
Core
Building reliable, high-performing systems and operationalizing machine learning models to support financial investment decisions, including alpha research, portfolio construction, and risk management.
Role type
Principal Quant Developer (MLOps)
Builds
Scalable, resilient analytical and software solutions for systematic investment strategies, data pipelines, APIs, and cloud-based infrastructure on AWS.
Domain
Quantitative finance, systematic investing, and machine learning operations (MLOps/LLMOps).
Deliverable
production ML models | product features | infrastructure
Required skills
Deep Python expertise (OOP, design patterns), SQL (Oracle, Snowflake), NoSQL and graph databases, batch scheduling (Autosys, Airflow), API development (FastAPI, Flask), AWS cloud architecture (Lambda, S3, EKS, EC2), Docker, Kubernetes, Jenkins, Linux, infrastructure-as-code, CI/CD pipelines, unit testing frameworks, test-driven development, quantitative methods (linear regression, time-series analysis), statistics, probability, portfolio construction, risk management, forecasting, simulation-based algorithms.
Preferred skills
Operationalizing ML models on AWS (SageMaker, Bedrock), MLflow, domain knowledge in equities/fixed income/alternative assets, CFA designation progress, GenAI initiatives.
Technologies
AWS, Airflow, FastAPI, Flask, Docker, Kubernetes, Jenkins, GitHub, SageMaker, Bedrock, MLflow, Oracle, Snowflake, Python, SQL, NoSQL, Linux, CI/CD
Responsibilities
Partner with quantitative researchers to prototype and deliver new systematic investment strategies; develop high-impact solutions across alpha research, portfolio construction, and risk management; design and implement scalable, resilient analytical and software solutions; lead research initiatives through the full software development lifecycle; build and maintain robust data pipelines, APIs, and event-driven systems; architect and support cloud-based solutions on AWS; create and manage CI/CD pipelines, containerized services, and infrastructure-as-code implementations; operationalize machine learning and AI models in production; apply quantitative and statistical techniques to develop forecasting, portfolio, and risk management tools; collaborate to translate research ideas into production-ready capabilities.
Seniority
Principal, hands-on IC with strategic leadership