Machine Learning Engineer

We are seeking a Machine Learning Engineer (MLOps focus) who will be a key technical contributor in advancing CTI’s artificial intelligence and machine learning capabilities. This role supports United States Special Operations Command (SOCOM) programs and other advanced defense initiatives by ensuring that ML models are not only trained effectively, but also deployed, monitored, and sustained in real-world operational environments, including edge-deployed systems. The successful candidate will be passionate about building and automating ML pipelines, implementing modern MLOps practices, and driving innovation that directly impacts mission outcomes. With hands-on experience taking ML models from concept to production deployment, this engineer will help CTI deliver highly reliable, scalable, and mission-ready ML solutions to the battlefield.

• Operationalize ML models by building robust pipelines for training, evaluation, deployment, and monitoring across diverse compute environments (cloud, on-prem, and edge).
• Collaborate with development teams and mission stakeholders to translate requirements into ML systems that can be deployed and sustained in operational settings.
• Implement CI/CD practices for ML, enabling automated testing, packaging, and deployment of models and data pipelines.
• Manage ML infrastructure and tooling, including containerization (Docker), orchestration (Kubernetes), and model serving platforms (e.g., Seldon, KServe, BentoML).
• Develop monitoring and observability systems to track model performance, data drift, and resource utilization, ensuring reliability in mission environments.
• Contribute to security and compliance in ML pipelines, ensuring model deployments meet defense and customer requirements.
• Explore and integrate modern MLOps technologies to improve reproducibility, scalability, and maintainability of ML capabilities.

Location: This position is fully onsite and requires work to be performed at MacDill Air Force Base in Tampa, Florida. Remote work is not available for this role.

Travel requirements: Willingness and ability to travel up to 25%.

• Bachelor’s degree in Computer Science, Electrical Engineering, Data Science, or a related technical discipline. Master’s degree preferred.
• 5+ years of professional experience in software engineering, machine learning, or related fields.
• Experience with MLOps tools and frameworks (MLflow, Kubeflow, Airflow, DVC, etc.).
• Proficiency in building and deploying containerized ML services (Docker, Kubernetes).
• Strong understanding of CI/CD pipelines and DevOps practices applied to ML.
• Familiarity with deep learning frameworks (PyTorch, TensorFlow) and their deployment requirements.
• Knowledge of monitoring and logging systems (Prometheus, Grafana, ELK/EFK stacks).
• Strong software engineering background (Python required; C, Rust, or MATLAB a plus).
• Active U.S. Government Secret clearance with SCI eligibility (TS/SCI).
• U.S. Citizenship is required as only U.S. citizens are eligible for a security clearance.

• Experience in DoD programs and drone (UAS) development.
• Experience working with diverse data types (RF signals, imagery, video, sensor feeds) is a plus.
• Experience deploying ML models to edge or constrained environments is highly desirable.
• Familiarity with secure software deployment in defense environments.
• Experience with air-gapped registries, offline updates, reproducible builds, and SBOM attestation in CI.
• Experience with Explainable AI/ML.