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Develop secure, data-rich frontend features using React and TypeScript for a DoD mission-planning platform. Integrate UIs with backend APIs, support map-based and AI-enabled decision-support workflows, write unit/integration tests, participate in code reviews and security remediation, and collaborate with cross-functional teams with periodic onsite sprint planning in Laurel, MD.

Design, build, test, and deploy secure mission-planning software. Maintain secure CI/CD, containerized deployments, automated testing, and security-scanning workflows. Integrate AI capabilities, investigate vulnerabilities, support remediation, produce technical design documentation, and collaborate with stakeholders and customer leads.

Build, test, and integrate secure backend services and APIs for DoD mission-planning. Work on REST and gRPC services, asynchronous workflows, geospatial databases, containerized deployments, CI/CD, automated tests, and vulnerability remediation while collaborating with AI/ML, frontend, and cybersecurity teams.

Integrate LLMs, RAG, and agentic-AI into secure decision-support software. Design prompts and inference workflows, connect AI to backend services, evaluate outputs, build tests, document designs, and collaborate with engineering, security, and DevSecOps teams to deliver reliable mission-capable AI features.

Operate and secure mission-critical cloud environments: monitor availability and security, resolve Tier 1-3 incidents, provision tenant AWS resources, administer Linux/Windows/AD, manage containers and Kubernetes, automate infrastructure with Bash/Ansible/Terraform, support DevSecOps pipelines, troubleshoot network/platform issues, and maintain operational documentation. Requires occasional weekend/holiday coverage.

The MLOps Engineer will manage the ML system lifecycle, deploy models in mission-critical environments, and ensure reliability through monitoring and infrastructure optimization. They will work with cloud-native technologies and focus on operationalizing AI/ML capabilities within classified settings.

The AI/ML Engineer will design and deploy machine learning solutions, build model architectures, collaborate with teams, and ensure model performance and scalability.

Design, build, and maintain enterprise SQL Server databases for a DoD healthcare program. Implement schemas, complex queries, stored procedures, HA/clustering, replication, backups, disaster recovery, RMF security controls, performance tuning, and support ETL/data integration.

Partner with stakeholders to gather requirements, validate integrated datasets, and deliver trusted insights via SQL-driven analysis and Tableau/Qlik dashboards to support federal healthcare operational decisions.

Design, build, and maintain cloud-native data pipelines (Ingest→Transform→Expose) using Airflow, Spark, dbt, and Iceberg. Ingest and normalize HL7/FHIR/OMOP and other structured/unstructured data for analytics and AI, implement schema versioning and governance, ensure interoperability and compliance with DoD Responsible AI and NIST frameworks, and collaborate with DevSecOps and Data Science teams to deliver production-ready datasets.