Contractor To Capability Lifecycle Ai​/Ml Engineer

LABOR CATEGORY 42: CONTRACTOR SUPPORT TO CAPABILITY LIFECYCLE AI/ML ENGINEER

Location:

Norfolk, VA, USA (On-site)

• 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.

• 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 life cycles.
• 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…