CTI
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. 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 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.
Requirements 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%.
Necessary Skills and Experience
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.
Beneficial Skills and Experience
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.
Salary range
$185,000 - $200,000 USD
Benefits
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
401(k) plan with Safe Harbor contribution
Tuition reimbursement/professional training options
Employee Assistance Program
Travel 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 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.
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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.
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 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.
Requirements 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%.
Necessary Skills and Experience
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.
Beneficial Skills and Experience
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.
Salary range
$185,000 - $200,000 USD
Benefits
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
401(k) plan with Safe Harbor contribution
Tuition reimbursement/professional training options
Employee Assistance Program
Travel 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 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.
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