Contractor to Capability Lifecycle AI-ML Engineer Security Clearance

Position: Contractor Support to Capability Lifecycle AI-ML Engineer with Security Clearance
Contractor Support to Capability Lifecycle AI-ML Engineer DEFTEC delivers mission-critical solutions through skillfully delivered services and innovative products. We are inspired by our clients' critical missions and driven to provide the most effective solutions to execute their missions, operational challenges, and requirements. Our dedicated, experienced, and talented employees work closely with our clients to ensure the delivery of exceptional services and products.

POSITION OVERVIEW This position provides specialized contractor support to the Capability Development Directorate at Headquarters Allied Command Transformation (HQ SACT), focusing on the integration of Artificial Intelligence (AI) and Machine Learning (ML) solutions across the capability lifecycle. The role is responsible for developing and implementing advanced data-driven models and analytics to enhance NATO's capability planning, prioritization, and decision-making processes. Working closely with the CAPDEV Data and Analytics Office (DAO), the AI/ML Engineer will design and optimize algorithms for requirements analysis, capability forecasting, and performance assessment, ensuring alignment with NATO's strategic objectives.

Additional responsibilities include supporting data governance, enabling self-service analytics, and contributing to the modernization of capability development through innovative AI/ML applications that improve interoperability, deployability, and sustainability of Alliance forces. JOB RESPONSIBILITIES
* Al/ML Model Development:
Design, develop, train, and deploy machine learning models to support forecasting, risk identification, readiness assessment, and decision support across the capability lifecycle.
* Advanced Analytics Integration:
Integrate Al/ML models into enterprise analytics workflows, dashboards, and reporting solutions to enable operational use by analysts and decision­makers.
* Data Preparation and Feature Engineering:
Develop and maintain data preparation pipelines, feature engineering processes, and training datasets in coordination with data engineering teams to ensure model accuracy, robustness, and traceability.
* Cloud-Based Al/ML Engineering:
Implement and operate Al/ML solutions within approved cloud environments, including model training, deployment, and orchestration using secure, scalable architectures.
* Model Lifecycle Management:
Establish and execute model validation, performance monitoring, retraining, and version control processes to ensure sustained accuracy and operational relevance of deployed models.
* Responsible Al Practices:
Apply responsible and explainable Al principles, including transparency, bias awareness, and interpretability, appropriate to defense and decision support contexts.
* Automation and Optimization:
Identify and implement opportunities to automate analytic workflows, model execution, and data processing to improve efficiency and reduce manual intervention.
* Prototyping and Experimentation:
Design and deliver proof-of-concept and prototype Al/ML solutions, including exploration of emerging techniques (e.g., large language models or incremental learning), aligned with DAO priorities.
* Performance and Scalability Optimization:
Optimize Al/ML pipelines and supporting infrastructure to ensure reliable performance under operational workloads and evolving data volumes.
* Technical Documentation:
Produce and maintain comprehensive technical documentation describing Al/ML models, data dependencies, assumptions, limitations, and operational integration points.
* Stakeholder Engagement:
Collaborate with analysts, engineers, and stakeholders to translate operational requirements into Al/ML solutions and explain analytic outputs to technical and non-technical audiences.
* Knowledge Transfer:
Deliver knowledge transfer, mentoring, and technical guidance to DAO personnel to support long-term sustainment of Al/ML capabilities.
* Security and Compliance:
Ensure Al/ML development and deployment comply with NATO and organizational security, data protection, and classification handling requirements.
* Capability Lifecycle Support:
Apply Al/ML expertise to support requirements-based planning, capability development, delivery monitoring, and performance assessment activities.
* Continuous Improvement:
Identify opportunities to enhance Al/ML methods, tooling, and practices in alignment with DAO's Decision Advantage objectives.
* Technical Support:
Provide ongoing technical support and troubleshooting for Al/ML models, pipelines, and integrated analytic solutions.
* Additional Tasks:
Perform additional tasks as required by the COTR in scope of this labor category. REQUIRED QUALIFICATIONS
* 8+ years of progressive professional experience in data science, advanced analytics, and/or machine learning engineering, including experience delivering operational analytics or decision-support solutions in complex enterprise environments.
* Demonstrated expertise in machine learning and…