AI/ML DevSecOps Engineer

Arcfield was purpose-built to protect the nation and its allies through innovations in digital transformation, space mission engineering and launch assurance, miniaturized sensors and satellites, advanced modeling and simulation, cybersecurity, and conventional and hypersonic missile support. Headquartered in Chantilly, VA with 16 global offices, Arcfield employs more than 1,500 engineers, analysts, IT specialists, and other professionals with more than 60 years of collective proven experience supporting missions in cyber and space defense, space exploration, hypersonic and nuclear deterrence and warfighter readiness. Visit arcfield.com for more details.

Overview: Seeking an experienced AI/ML DevSecOps Engineer who excels in developing, deploying, and securing high-performance machine learning solutions. The ideal candidate will demonstrate strong competence in CI/CD pipeline implementation and troubleshooting, ensuring seamless integration of security best practices at every stage of the software development lifecycle. This role combines deep expertise in AI/ML with DevSecOps principles to drive innovation, reliability, and security throughout our technology stack.

• Subject Matter Expertise: Serve as a recognized authority by tackling unusually complex problems with highly innovative solutions.
• Decision Making: Independently determine and execute strategies aligned with organizational goals and project requirements.

• Algorithm Creation: Develop and program integrated software algorithms to structure, analyze, and leverage both structured and unstructured data.
• Insight Generation: Create descriptive, diagnostic, predictive, and prescriptive analytics/algorithms to extract actionable insights.
• Machine Learning Techniques: Apply advanced methods such as decision trees, logistic regression, Bayesian analysis, neural networks, and other ML techniques to enhance product/system performance.

• Code Development: Translate algorithms and technical specifications into robust, maintainable code using current programming languages and technologies (e.g., Python, Julia, Rust, C++, ...).
• Testing & Debugging: Implement best practices for efficient software testing and thorough debugging to deliver reliable and secure products.
• Performance Optimization: Continuously identify and implement efficiencies that boost performance and reduce complexity.

• Pipeline Engineering: Design, implement, and maintain resilient CI/CD pipelines that support rapid iteration, continuous integration, and secure deployments of AI/ML solutions.
• Security Integration: Integrate security scanning, code reviews, vulnerability assessments, and compliance checks into pipelines, ensuring DevSecOps best practices are upheld.
• Infrastructure as Code (IaC): Employ tools like Terraform, Ansible, or similar for automated infrastructure provisioning and management, emphasizing security and reproducibility.
• Troubleshooting & Optimization: Diagnose and resolve pipeline issues quickly, optimizing build, test, and deployment processes to maintain operational excellence and security.

• Platform Contribution: Collaborate on the design, build, and maintenance of internal development platforms that streamline the developer experience and enhance CI/CD workflows.
• Automated Tooling: Contribute to creating automated security, testing, and monitoring tools that integrate seamlessly within the development platform.

• Deep Learning Integration: Apply deep learning technologies for enhanced visualization, automated learning, and adaptable response mechanisms.
• Emerging Tech: Develop ML solutions aligned with cutting-edge innovations in VR, AR, robotics, and other advanced computational fields.
• Big Data Frameworks: Leverage large-scale computing frameworks (e.g., Spark, Hadoop) and data analysis systems to handle high volumes of data efficiently and securely.

• Statistical Foundations: Utilize strong knowledge of experimental design, statistical analysis, optimization, and probability theory to drive data-driven decisions.
• Tool Development: Build and maintain tools for data processing, model training, and system monitoring using languages such as Python/R and specialized ML frameworks.
• Security Best Practices: Implement DevSecOps methodologies, ensuring compliance with industry standards and regulations throughout the software development lifecycle.

• Clear Documentation: Produce detailed documentation and procedures for installation, maintenance, and security controls of AI/ML systems.
• Lifecycle Support: Ensure ongoing updates, patches, and improvements for both infrastructure and applications to maintain a secure and high-performing environment.

• BS 10-12, MS 8-10, Phd 5-7
• Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Equivalent work experience also considered.
• Must possess and have the ability to maintain a Secret clearance
• Experience: Proven track record of designing and managing CI/CD pipelines, with hands-on expertise in DevSecOps practices and tooling (e.g., Jenkins, GitLab CI, CircleCI, or Azure DevOps).
• ML & Software Skills: Proficiency in machine learning frameworks (TensorFlow, PyTorch, Scikit-learn), programming languages (Python, C++, Rust), and statistical analysis.
• Security Focus: Demonstrated experience incorporating vulnerability scanning, threat modeling, and security gates into CI/CD workflows.
• Containers & Orchestration: Experience with containerized environments (Docker/Kubernetes) and associated security practices.
• Collaboration & Communication: Excellent problem-solving skills and the ability to communicate complex technical concepts to both technical and non-technical stakeholders.

EEO Statement: We are an equal opportunity employer and federal government contractor. We do not discriminate against any employee or applicant for employment as protected by law.