Machine Learning Engineer

Position Title: Machine Learning Engineer

At CTI, a Parsons Company, we deliver cutting‑edge technology solutions that empower end‑users to SEE (Sense, Evaluate, Effect) the invisible battlespace. Our mission is to provide critical capabilities across spectrum operations, communications, cyberspace, information space, human terrain, gray space, geo‑intelligence, modeling & simulation, ISR (Intelligence, Surveillance & Reconnaissance), JADC2 (Joint All‑Domain Command and Control), covert operations, and more. These integrated solutions drive mission success, close the Kill Chain, and ensure operational superiority.

Founded in April 2000, CTI was built with a clear purpose: to provide user‑focused, high‑end software and systems development for the Defense and Intelligence communities. Our team of highly skilled, technically diverse developers works hand‑in‑hand with military personnel to design and deliver solutions that make a real impact on the fight.

Headquartered in Prince Frederick, Maryland, and supported by a robust remote workforce nationwide, CTI is strategically positioned to serve a wide range of DoD and agency customers. As part of Parsons Corporation, we’ve expanded our capabilities and strengthened our commitment to delivering mission‑critical solutions that matter.

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.

• 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 Bento
  
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• 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.

Location: This is a fully onsite position based at Mac Dill 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 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 Dev Ops practices applied to ML.
• Familiarity with PyTorch, Tensor Flow and deployment best practices.
• Knowledge of monitoring and logging systems (Prometheus, Grafana, ELK/EFK stacks).
• Proficiency…