CONTRACTOR SUPPORT TO CAPABILITY LIFECYCLE AI/ML ENGINEER

DESCRIPTION

Deputy Chief of Staff Capability Development (DCOS CAPDEV) acts as the Supreme Allied Commander Transformation's Director for guidance, direction and co-ordination of the activities and resources of the Capability Development Directorate. CAPDEV is responsible to:

ü  Identify and prioritize Alliance capability development from short to long term, ensuring coherence between all capabilities within the CAPDEV portfolio.

ü  Lead the determination of required capabilities and prioritization of shortfalls to inform the delivery of materiel and non-materiel solutions across the Doctrine, Organisation, Training, Material, and Leadership, Personnel, Facilities and Interoperability (DOTMLPFI) lines of effort to enable a holistic approach to capability development, ensuring improved interoperability, deployability and sustainability of Alliance Forces.

The future Capability Development Directorate will include enduring functionality to effectively plan and manage coherent through life capability development, aligned to NATO’s strategic intent and priorities. The CAPDEV Data and Analytics Office (DAO) is responsible to DCOS for managing the data and platform operations for Capability Lifecycle, Requirements, and P3M data as well as providing analytics as service and enabling self-service analytics for CAPDEV decision makers.

As part of ongoing organisational functional reviews, CAPDEV is in the process of implementing measures for improved capability development planning and management, including the way it collects, manages, analyses and reports on capability development and delivery information, both legacy and current.

EXPERIENCE AND EDUCATION:

Essential Qualifications/Experience:

·         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 statistical modeling, including development, training, validation, and deployment of models supporting forecasting, risk analysis, performance assessment, or decision support across business or capability lifecycles

·         Demonstrated experience designing and operating automated data pipelines, including ETL/ELT workflows, feature engineering, and data transformation processes to support analytics and AI/ML workloads

·         Demonstrated professional experience with cloud-based analytics and AI/ML platforms, including deployment and operation of models and data pipelines in secure, scalable cloud environments

·         Bachelor’s degree in Data Science, Computer Science, Mathematics, Engineering, Statistics, or a related quantitative discipline

·         Demonstrated experience integrating AI/ML solutions into enterprise analytics tools, dashboards, or reporting platforms to support operational use by analysts and decision-makers

·         Demonstrated experience with model lifecycle management, including performance monitoring, retraining strategies, version control, documentation, and optimization for production environments

·         Demonstrated experience working within governed or regulated environments, including adherence to data governance, security, and compliance requirements relevant to defence, security, or other highly regulated domains

·         Demonstrated ability to collaborate across multidisciplinary teams, including analysts, data engineers, platform engineers, and system administrators, to deliver interoperable, production-ready analytics solutions

·         Demonstrated ability to communicate complex analytical and AI/ML concepts clearly to both technical and non-technical stakeholders, supporting effective adoption and operational use of delivered solutions. Demonstrated minimum NATO or National SECRET clearance with the appropriate national authority for the duration of the contract

·         Demonstrable proficiency in effective oral and written communication, including briefing and coordinating with business stakeholders

DUTIES/ROLE: 

·         AI/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 AI/ML models into enterprise analytics workflows, dashboards, and reporting solutions to enable operational use by analysts and decision-makers

·         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 AI/ML Engineering: Implement and operate AI/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 AI Practices: Apply responsible and explainable AI principles, including transparency, bias awareness, and interpretability, appropriate to defence 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 AI/ML solutions, including exploration of emerging techniques (e.g., large language models or incremental learning), aligned with DAO priorities

·         Performance and Scalability Optimization: Optimize AI/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 AI/ML models, data dependencies, assumptions, limitations, and operational integration points

·         Stakeholder Engagement: Collaborate with analysts, engineers, and stakeholders to translate operational requirements into AI/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 AI/ML capabilities

·         Security and Compliance: Ensure AI/ML development and deployment comply with NATO and organizational security, data protection, and classification handling requirements

·         Capability Lifecycle Support: Apply AI/ML expertise to support requirements-based planning, capability development, delivery monitoring, and performance assessment activities

·         Continuous Improvement: Identify opportunities to enhance AI/ML methods, tooling, and practices in alignment with DAO’s Decision Advantage objectives

·         Technical Support: Provide ongoing technical support and troubleshooting for AI/ML models, pipelines, and integrated analytic solutions

·         Additional Tasks: Perform additional tasks as required by the COTR in scope of this labor category