Senior Machine Learning Engineer

Job Summary We are seeking a Senior Machine Learning Engineer to design, develop, and scale production-grade machine learning solutions supporting enterprise decisioning platforms. The ideal candidate will have strong expertise in Python, AWS, MLOps, and cloud-native architectures, with experience deploying and operationalizing machine learning models in production. This role collaborates closely with Data Scientists, Product Managers, and Engineering teams to build scalable ML platforms that support business-critical applications. Key Responsibilities Design, develop, and deploy production-grade machine learning solutions on AWS. Build and maintain scalable ML pipelines for model training, validation, deployment, monitoring, and lifecycle management. Partner with Data Scientists to operationalize machine learning and advanced analytics models. Develop cloud-native infrastructure to support machine learning workloads. Optimize model performance, scalability, reliability, and operational efficiency. Implement best practices for testing, CI/CD, governance, monitoring, and MLOps. Support enterprise machine learning initiatives for credit decisioning, fraud detection, risk assessment, and partner applications. Contribute to the evolution of enterprise ML platforms and engineering standards. Develop and maintain workflow orchestration for machine learning pipelines. Monitor production models and implement governance and observability best practices. Collaborate with cross-functional teams to deliver scalable AI and machine learning solutions. Required Qualifications 5+ years of experience in Machine Learning Engineering, Software Engineering, or a related field. Strong proficiency in Python. Experience with Python libraries including Spark, Pandas, and NumPy. Strong experience with AWS services including EC2, ECS, EKS, and Amazon S3. Experience designing and deploying production machine learning applications. Strong understanding of MLOps principles and the complete machine learning lifecycle. Experience building and maintaining ML pipelines and deployment workflows. Experience with distributed data processing frameworks such as Apache Spark. Strong software engineering fundamentals, including version control, automated testing, and CI/CD practices. Experience working with cloud-native architectures. Strong analytical, troubleshooting, and problem-solving skills. Ability to work in a hybrid environment based in McLean, VA. Preferred Qualifications Experience with Databricks. Strong SQL and data analysis skills. Experience building end-to-end machine learning platforms. Familiarity with feature stores, model monitoring, and ML observability tools. Experience supporting large-scale enterprise machine learning environments. Exposure to Generative AI, Large Language Models (LLMs), or AI platform engineering. Hands-on experience with Kubernetes and Docker. Experience with workflow orchestration tools such as Kubeflow and Apache Airflow. Certifications AWS Solutions Architect or related AWS cloud certification preferred. Education: Bachelors Degree Certification: AWS Solutions Architect