CONTRACTOR SUPPORT TO CAPABILITY LIFECYCLE AI/ML ENGINEER

About the role

Supports contractor operations by developing, deploying, and optimizing AI/ML models for forecasting, risk identification, readiness assessment, and decision support across the capability lifecycle.

Responsibilities

• Design, develop, train, and deploy machine learning models to support various business needs.
• Integrate AI/ML models into enterprise analytics workflows and dashboards.
• Develop and maintain data preparation pipelines, feature engineering processes, and training datasets.
• Implement and operate AI/ML solutions within secure, scalable cloud environments.
• Establish and execute model validation, performance monitoring, retraining, and version control processes.
• Apply responsible and explainable AI principles in defence and decision-support contexts.
• Identify and implement opportunities to automate analytic workflows and data processing.
• Design and deliver proof-of-concept and prototype AI/ML solutions, including exploration of emerging techniques.
• Optimize AI/ML pipelines and supporting infrastructure for reliable performance under operational workloads.
• Produce and maintain comprehensive technical documentation on AI/ML models, data dependencies, and operational integration points.
• Collaborate with analysts, engineers, and stakeholders to translate operational requirements into AI/ML solutions and explain analytic outputs.
• Deliver knowledge transfer, mentoring, and technical guidance to DAO personnel.
• Ensure compliance with NATO and organizational security, data protection, and classification handling requirements.
• Apply AI/ML expertise to support requirements-based planning, capability development, delivery monitoring, and performance assessment activities.
• Identify opportunities to enhance AI/ML methods, tooling, and practices in alignment with DAO’s Decision Advantage objectives.
• Provide ongoing technical support and troubleshooting for AI/ML models, pipelines, and integrated analytic solutions.

Requirements

• 8+ years of progressive professional experience in data science, advanced analytics, and/or machine learning engineering.
• 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.
• Demonstrated experience designing and operating automated data pipelines, including ETL/ELT workflows, feature engineering, and data transformation processes.
• 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.
• 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.
• Demonstrated ability to collaborate across multidisciplinary teams, including analysts, data engineers, platform engineers, and system administrators.
• Demonstrated ability to communicate complex analytical and AI/ML concepts clearly to both technical and non-technical stakeholders.
• Minimum NATO or National SECRET clearance with the appropriate national authority.
• Proficiency in English as defined in STANAG 6001 (Standardized Linguistic Profile (SLP) 3333 - Listening, Speaking, Reading and Writing).

Qualifications

• Minimum NATO or National SECRET clearance with the appropriate national authority.
• Proficiency in English as defined in STANAG 6001 (Standardized Linguistic Profile (SLP) 3333 - Listening, Speaking, Reading and Writing).

Skills

• Machine Learning
• Data Science
• Advanced Analytics
• Cloud Computing
• Model Deployment
• Data Pipelines
• Feature Engineering
• Automated Processes
• Documentation
• Collaboration
• Communication
• Security Compliance

Benefits

• Competitive compensation package
• Flexible working hours
• Professional development opportunities
• Health and wellness programs

Pay

• Competitive salary based on experience and qualifications

Schedule

• Full-time position