AI/ML Engineer

Fidelity Investments•Jersey City, NJ

5d•$97,000 - $185,000•Onsite

About The Position

The Fidelity Risk Group (FRG) is seeking a Senior AI/ML Engineer to help bring machine learning models from development into reliable, production-ready deployment. This is a hands-on engineering role focused on operationalizing AI/ML systems by building repeatable deployment patterns, implementing model monitoring, and improving the processes that move trained models from data scientists into production. This role works most closely with data scientists and serves as the engineering partner responsible for turning trained model artifacts into stable, observable, and supportable production systems. The ideal candidate enjoys building production-quality ML systems, improving reliability, and creating the technical patterns and processes that make model deployment faster, safer, and easier to scale over time. The role has a strong emphasis on: Model monitoring and drift detection, Deployment and production readiness, Repeatable handoff processes from data science to production, Production-grade Python, testing, and CI/CD discipline, AWS/SageMaker-based ML deployment patterns. This is a Senior role and is expected to be primarily hands-on, with the ability to raise technical standards through direct execution and practical process improvement.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems, Data Science, Analytics, Mathematics, Statistics, or a closely related field and four (4) years of experience as an AI/ML Engineer, Machine Learning Engineer, MLOps Engineer, Software Engineer, or a closely related occupation. Or, alternatively, Master’s degree in Computer Science, Engineering, Information Technology, Information Systems, Data Science, Analytics, Mathematics, Statistics, or a closely related field and two (2) years of experience as an AI/ML Engineer, Machine Learning Engineer, MLOps Engineer, Software Engineer, or a closely related occupation.
Strong Python skills for production ML systems, including experience writing tests and maintainable code
Strong SQL skills, including the ability to work comfortably with production-facing datasets
Experience with Snowflake or similar cloud data platforms
Experience with model monitoring, drift detection, and statistical approaches to monitoring
Experience working in AWS, with SageMaker strongly preferred
Experience using Git/source control and CI/CD workflows for production systems
Comfort working in Unix/Linux command-line environments

Nice To Haves

Experience with Docker or other containerization technologies
Familiarity with model registry, artifact versioning, and promotion workflows
Experience with experiment tracking tools
Experience with observability tooling such as Datadog, OpenTelemetry, CloudWatch, Grafana, or similar platforms
Familiarity with A/B testing, champion/challenger patterns, or controlled rollout approaches for ML systems
Experience with orchestration or workflow automation tools
Experience with GenAI or LLM systems is a plus, but not required

Responsibilities

Partner with data scientists early in the model development lifecycle to ensure production readiness, including feature source-ability and ground-truth capture for monitoring.
Build and mature the process for operationalizing machine learning models in production so deployments are repeatable, auditable, and fast.
Take trained model artifacts through packaging and deployment using repeatable AWS and SageMaker patterns.
Design and implement automated monitoring for production models, including drift detection, alerting, and model health evaluation.
Tune monitoring workflows to reduce false positives and ensure alerts are meaningful, actionable, and operationally useful.
Create reusable deployment and observability patterns that can scale across multiple AI/ML projects.
Develop production-grade Python code with strong testing and maintainability practices.
Work within Git-based development workflows and CI/CD pipelines to support reliable model promotion and release management.
Support a mix of batch and real-time model deployment patterns based on business and technical needs.
Contribute to operational readiness through lightweight support participation, runbooks, and incident documentation.