JPC - 206356 - ML Engineer

Senior Machine Learning Engineer – McLean, VA (Hybrid, 12+ Month Contract)

About the Role

Join a high-impact Machine Learning Engineering team at a leading financial services organization, driving production ML systems for critical platforms such as credit decisioning, fraud detection, and risk assessment. As a Senior Machine Learning Engineer, you will partner with cross-functional teams to develop, scale, and optimize cloud-native ML solutions. This is an opportunity to shape enterprise-scale AI platforms, influence engineering standards, and work with the latest cloud and MLOps technologies.

Responsibilities

• Design, develop, and deploy production-grade machine learning solutions on AWS

• Build and maintain scalable ML pipelines for model training, validation, deployment, and monitoring

• Collaborate with Data Scientists to operationalize advanced ML models

• Develop and optimize cloud-native infrastructure to support enterprise ML workloads

• Implement best practices across the ML lifecycle, including testing, CI/CD, governance, and monitoring

• Support and enhance platforms powering credit decisioning, fraud detection, risk assessment, and partner programs

• Contribute to the evolution of ML platform capabilities and engineering standards

• Required Skills and Experience

• 5+ years in Machine Learning Engineering, Software Engineering, or related fields

• Proficiency in Python (Spark, Pandas, NumPy)

• Deep expertise in AWS (ECS, EC2, EKS, S3), cloud-native architectures

• Strong understanding of MLOps principles and production ML deployment

• Experience with distributed data processing (Apache Spark)

• Solid software engineering fundamentals: version control, testing, CI/CD practices

• Hybrid on-site work in McLean, VA (Tuesday–Thursday)

• Preferred Skills

• Experience with Databricks and modern analytics platforms

• Strong SQL and data analysis background

• End-to-end ML platform development

• Familiarity with feature stores, model monitoring, and ML observability

• AWS Solutions Architect or similar certifications

• Hands-on with Kubernetes, Kubeflow, workflow orchestration tools

• Exposure to Generative AI, LLM deployment, or AI platform engineering

Benefits

• Work on mission-critical AI platforms at enterprise scale

• Collaborative, cross-functional team environment

• Opportunity to drive innovation in financial services ML applications

• Professional growth with exposure to the latest cloud, MLOps, and AI technologies

• Competitive compensation on a long-term contract

• How to Apply If you’re passionate about building and scaling production ML systems, and want to make an impact in financial services, apply now with your updated resume.