KP AI​/ML Engineer

Job Overview

Apex Systems is looking for an AI/ML Engineer to support our client's government program in Norfolk, Virginia.

• Must be local and be willing to go onsite in Norfolk, VA.
• Must have an active Secret clearance to be considered.
• Must have completed a Bachelor's degree in Data Science, Computer Science, Mathematics, Engineering, Statistics, or a related quantitative discipline.
• 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.
• 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 proficiency in English.
• Demonstrable proficiency in effective oral and written communication, including briefing and coordinating with business stakeholders.

• 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…