CTI, a Parsons Company, delivers cutting-edge technology solutions that enable end-users to Sense, Evaluate, and Effect (SEE) the invisible battlespace. Our mission is to provide critical capabilities across various operations including spectrum, communications, cyberspace, and intelligence. Founded in April 2000, CTI focuses on providing user-focused, high-end software and systems development for the Defense and Intelligence communities. Headquartered in Prince Frederick, Maryland, with a robust remote workforce, CTI serves a wide range of DoD and agency customers. As part of Parsons Corporation, we are committed to delivering mission-critical solutions.

About the Position

Introduction

CTI, a Parsons Company, is looking for a Machine Learning Engineer with an MLOps focus to advance our AI and machine learning capabilities. This role supports United States Special Operations Command (SOCOM) programs and other advanced defense initiatives, focusing on deploying, monitoring, and sustaining ML models in real-world operational environments, including edge-deployed systems. The ideal candidate will have deep expertise in MLOps, model deployment, and infrastructure automation to build scalable, secure, and production-grade ML systems and will be passionate about building and automating ML pipelines.

Responsibilities

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

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 Top Secret clearance with SCI eligibility (TS/SCI).
U.S. Citizenship is required as only U.S. citizens are eligible for a security clearance.

Nice to Have

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.

Benefits

CTI, a Parsons Company, offers:

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