Senior Machine Learning Engineer - Hybrid (DMV Area) with Security Clearance

Job Description This role applies modern machine learning techniques, data engineering practices, and platform-based development to deliver scalable, secure, and operational AI solutions. The ideal candidate will have hands-on experience with Palantir Foundry, strong Python and data pipeline development skills, and the ability to integrate, train, deploy, and optimize ML models across enterprise environments. This role supports both development and operationalization of AI/ML capabilities to enable mission-critical decision support and automation. Key Responsibilities Design, develop, train, and deploy machine learning models using enterprise data platforms and modern ML frameworksBuild and maintain robust ETL/ELT pipelines and complex data workflows, ensuring data quality, lineage, and version controlDevelop pipelines and custom logic within Palantir Foundry and Forge platformsDefine and leverage Foundry Ontology to model, organize, and link enterprise dataBuild user workflows and operational applications in Foundry (low-code/no-code + custom code)Implement and integrate LLM-based capabilities via Python-based APIs and librariesConduct feature engineering, model evaluation, and performance tuningCollaborate with data scientists, software engineers, and mission stakeholders to deploy AI into production environmentsImplement cloud-based ML deployment best practices (AWS/Azure)Develop dashboards, visualizations, and interactive analytic tools for stakeholdersEnsure system and data security practices align with federal cybersecurity standardsDocument technical processes, pipelines, and model lifecycle procedures Required Qualifications Must hold a current Secret ClearanceBA/BS in Computer Science, Engineering, Data Science, Mathematics, or related field OR equivalent experience5+ years' experience with machine learning, data engineering, or platform-based AI deliveryHands-on experience with Palantir FoundryProficiency in Python for ML and pipeline developmentExperience implementing and calling LLM APIs and LLM-driven applicationsProficiency with SQL, PySpark / SparkExperience building data pipelines and integrating large-scale data from multiple sourcesExperience with cloud environments (AWS and/or Azure)Experience with Machine learning model design, training, and deploymentExperience with LLM integration & API-driven AI developmentExperience with ETL/ELT pipeline engineeringExperience with data quality, governance, and version controlExperience with full-stack data platform application developmentStrong problem-solving and collaboration skillsClear, mission-focused communication Desired Qualifications Data security best practices and secure AI deploymentModel monitoring and MLOps toolsDashboards and analytic tools (e.g., Foundry Contour, Power BI, Tableau)Palantir Foundry pipelines & ontology modeling