Machine Learning Engineer
Cormac Corporation•Leesburg, VA
•Remote
About The Position
CORMAC is seeking a Machine Learning Engineer to be responsible for designing, developing, and optimizing machine learning solutions for a cloud-based CMS data platform supporting large-scale analytics, predictive modeling, automation, and decision support across enterprise healthcare data environments. Serves as the technical SME for machine learning architecture, model development, MLOps, and AI-driven solutions leveraging platforms such as Databricks, cloud-native services, and advanced analytics tools.
Requirements
- Bachelor's degree in computer science/engineering related or equivalent degree
- 4+ years of experience in Information Technology (IT) and the software development lifecycle (SDLC)
- Strong experience building and deploying enterprise machine learning solutions in cloud environments
- Expertise in Python, SQL, Spark, ML frameworks (Scikit-learn, TensorFlow, PyTorch, XGBoost, or similar)
- Experience with Databricks ML workflows, notebooks, feature engineering, and model deployment
- Strong understanding of MLOps, model lifecycle management, and production ML systems
- Knowledge of healthcare data analytics, data governance, and regulatory compliance considerations
- Experience with large-scale structured and unstructured datasets and distributed data processing
- Strong analytical, problem-solving, and stakeholder communication skills
- Ability to explain complex ML concepts to technical and non-technical audiences
- Experience supporting Centers for Medicare & Medicaid Services or other federal healthcare programs
- Familiarity with AWS Sage Maker, Azure ML, Snowflake, Tableau, Power BI, or Amazon QuickSight
Nice To Haves
- Master’s degree in Computer Science, Data Science, Machine Learning, Statistics, Engineering, or related field
- Certifications such as AWS Machine Learning Specialty, Databricks Machine Learning Associate/Professional, Azure AI Engineer, or related AI/ML certifications
Responsibilities
- Design, develop, train, validate, and deploy machine learning models to support predictive analytics, anomaly detection, classification, forecasting, and operational optimization
- Serve as the primary SME for machine learning strategy, architecture, and best practices across the program
- Build scalable ML pipelines using Databricks, Spark, Python, SQL, and cloud-native ML services
- Support use cases involving healthcare analytics, claims analysis, fraud detection, utilization forecasting, and operational performance improvement
- Collaborate with data architects, data engineers, business analysts, and stakeholders to translate business requirements into ML solutions
- Design and implement MLOps frameworks for model versioning, monitoring, retraining, CI/CD integration, and production deployment
- Optimize feature engineering, data preparation, model performance, and inference efficiency across large-scale datasets
- Establish model governance processes including explain ability, bias detection, validation, auditability, and compliance with federal and healthcare standards
- Support integration of ML outputs into online portals, reporting platforms, dashboards, and downstream operational systems
- Develop technical documentation, model design artifacts, and architecture recommendations for leadership and governance reviews
- Evaluate emerging AI/ML technologies and recommend modernization strategies aligned with CMS mission objectives
- Provide technical leadership, mentoring, and SME guidance to engineering and analytics teams
Benefits
- flexible work schedules
- remote, hybrid, or fully in-person workplace options
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Number of Employees
11-50 employees
