CTI, a Parsons Company is a high-tech software, systems engineering, and operational support corporation dedicated to providing cutting-edge engineering, and system development and support. We provide operationally-focused technology solutions for military and security applications. A veteran-owned company, CTI is committed to developing the next generation of advanced technologies in a friendly, product, and customer-focused environment. CTI specializes in developing software solutions that enable the collection, aggregation, transport, and visualization of highly complex data sets in a meaningful context to the warfighter.

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. We’re seeking a Machine Learning Engineer with deep expertise in MLOps, model deployment, and infrastructure automation to build scalable, secure, and production-grade ML 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.

Responsibilities include, but are not limited to:

• Operationalize ML models by building robust pipelines for training, evaluation, deployment, and monitoring across diverse compute environments (cloud, on-prem, and edge).
• Collaborate with cross-functional teams to translate mission requirements into deployable ML systems.
• Implement CI/CD for ML workflows, enabling automated testing, packaging, and deployment of models and data pipelines
• Manage ML infrastructure using Docker, Kubernetes, and model serving platforms like Seldon, KServe, or BentoML
• Develop monitoring and observability systems to track model performance, data drift, and resource utilization using tools like Prometheus, Grafana, and ELK/EFK stacks
• 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.

Requirements

Location: This is a fully onsite position based 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%.

Necessary Skills and Experience

• Bachelor’s degree in Computer Science, Electrical Engineering, Data Science, or a related technical discipline. (Master’s 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 PyTorch, TensorFlow, and deployment best practices.
• Knowledge of monitoring and logging systems (Prometheus, Grafana, ELK/EFK stacks).
• Proficiency in Python (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.

Beneficial Skills and Experience

• Prior work on DoD programs, UAS/drone systems, or defense AI applications.
• Experience working with diverse data types (RF signals, imagery, video, sensor feeds).
• Experience deploying ML models to edge or constrained environments.
• 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.