Role Description 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. - 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 explainability, 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. Qualifications - 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. Preferred Skills & Experience - 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. Location Leesburg, VA Work Arrangement 100% Remote Why CORMAC? At CORMAC, we leverage the power of data management and analytics to enable our customers to achieve their strategic goals. With over 20 years of experience in health information technology (HIT), human-centered design principles, and Agile development methodologies, CORMAC delivers complex digital solutions to solve some of the most challenging problems facing public healthcare programs today. - As a US Federal Government contractor in the public healthcare sector, our work is impactful and cutting-edge while being performed in a supportive, collaborative, and welcoming environment. - We offer flexible work schedules with remote, hybrid, or fully in-person workplace options to empower our employees to decide the workplace most suitable for them. - At CORMAC, we have a highly diverse workforce and believe a work environment is a place where creativity, collaboration, enthusiasm, and innovation happen, regardless of location. US Citizenship Required /E-Verify Participation/EEO As an Equal Employment Opportunity employer, CORMAC provides equal employment opportunity to all employees and applicants without regard to an individual's protected status, including race/ethnicity, color, national origin, ancestry, religion, creed, age, gender, gender identity/expression, sexual orientation, marital status, parental status, including pregnancy, childbirth, or related conditions, disability, military service, veteran status, genetic information, or any other protected status.

• Build and improve ML components across data, training, evaluation, and inference. • Fine-tune and adapt models as part of larger production systems. • Implement evaluation and testing to understand model behavior. • Help build and maintain data pipelines for real-world and synthetic data. • Debug model issues, performance problems, and production incidents. • Ship improvements iteratively and learn from real user feedback. • Work closely with senior ML engineers and product teams. • Work under real production constraints: latency, cost, reliability, and safety

• Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment. • Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. • Architect and operate scalable inference systems, balancing latency, cost, and reliability. • Design and maintain data systems for high-quality synthetic and real-world training data. • Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. • Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. • Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. • Make pragmatic trade-offs and ship improvements quickly, learning from real usage. • Work under real production constraints: latency, cost, reliability, and safety

• Build core ML systems that power a proactive, long-horizon AI product. • Own work end-to-end: data preparation, training, evaluation, inference, and iteration. • Turn research ideas into working systems that run reliably in production. • Debug model failures and system issues using real production signals. • Iterate quickly: ship, measure outcomes, refine, and repeat. • Collaborate closely with research, product, and engineering to deliver real user impact. • Mentor and review work from other ML engineers through example and technical judgment. • Work under real production constraints: latency, cost, reliability, and safety