Machine Learning Engineer at Chesapeake Technology International – Aiea, Hawaii
About This Position
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, weve 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 CTIs 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. Were 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.
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- Bachelors degree in Computer Science, Electrical Engineering, Data Science, or a related technical discipline. (Masters 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 Top Secret clearance with SCI eligibility (TS/SCI).
- U.S. Citizenship is required as only U.S. citizens are eligible for a security clearance.
- 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.
CTI, a Parsons Company, is a rapidly growing company offering the following:
- Medical, dental and vision insurance
- H.S.A. (partially funded by CTI) and Flex Spending
- Company-paid life insurance/AD&D and disability insurance
- Optional supplemental life, critical illness, hospital indemnity and accident insurances
- Paid vacation, sick leave and holidays
- 401k plan with Safe Harbor contribution
- Tuition reimbursement/professional training options
- Employee Assistance Program
- Travel Assistance
- Financial Planning Assistance
- Voluntary Pre-Paid Legal
- Flexible schedules with telecommuting options
- Service awards program
CTI is an Equal Opportunity employer and shall abide by the requirements of 41 CFR 60-1.4(a), 60-300.5(a) and 60-741.5(a). These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. Moreover, these regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status.