Lifecycle AI/ML Engineer 

Spektrum supports apex purchasers (NATO, UN, EU and National Government and Defence) and their Tier 1 supplier ecosystem with a wide range of specialist services. We provide our clients with professional services, specialised aerospace and defence sales, delivery, and operational subject matter expertise. We are looking for personnel to join our team and support key client projects.

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Who we are supporting

Allied Command Transformation (ACT) is NATO’s leading agent for change: driving, facilitating, and advocating the continuous improvement of Alliance capabilities to maintain and enhance the military relevance and effectiveness of the Alliance. The main objectives of ACT are: providing appropriate support to NATO missions and operations; leading NATO military transformation; and improving relationships, interaction and practical cooperation with partners, nations and international organisations. ACT therefore leads Alliance concept development, capability development, training and lessons-learned initiatives and provides unfettered military support to policy development within NATO.

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The program 

Capability Development & Management Support (CDMS)

 DCOS Capability Development (CAPDEV) acts as the Supreme Allied Commander Transformation's Director for guidance, direction and coordination of the activities and resources of the Capability Development Directorate.

The Requirements Division will execute all tasks and activities needed to support requirements management for NATO capabilities

The Capability Requirements (CR) Branch will develop the Capability Requirements Brief and recommended courses of action to resolve issues through the life cycle management of requirements using matrixed, cross-functional project-specific Requirements Management teams.

The Requirements Forward Branch (Mons) is responsible for conducting requirements development and management representation and engagement-related functions in Europe.

The Capability Division coordinates the development of capabilities from capability planning through acceptance and then disposal with management entities, NATO Headquarters staff and the NATO Governance Structure

The Capability Forward Branch (Mons) coordinates with ACO, NCIA, NSPA, NATO HQ, territorial Host Nations, and NATO Centres of Excellence (COEs) to support the development of capabilities.

Strategic Plans and Policy (SPP) supports Allied Command Transformation in formalizing military advice to shape future military strategy, political guidance, and other policy documents supporting NATO’s strategic objectives.

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Role ID: ACT-CDMS-26-02-B-42

Role Duties and Responsibilities

• 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:
  • Identify and prioritize Alliance capability development from short to long term, ensuring coherence between all capabilities within the CAPDEV portfolio.
  • 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.
• Taskings — Capability Lifecycle AI/ML Engineer
• 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 Line Manager. in scope of this labor category.
• 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.
• 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.
• Demonstrable proficiency in effective oral and written communication, including briefing and coordinating with business stakeholders.

 Essential Skills and 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.

Education

• 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.

 Language Proficiency

• Advanced Proficiency in English

 Working Location

• Norfolk, VA, USA

Working Policy

• On-Site

Contract Duration

• July 2026 – December 2030

Security Clearance

• Valid National or NATO Secret personal security clearance