LMI seeks an experienced AI Engineer to support the U.S. Army Center for Initial Military Training’s (CIMT) Holistic Health & Fitness Management System (H2FMS). H2FMS is a secure analytics environment hosted in Army GovCloud that integrates data from the vendor-provided H2F data capture application and provides multidomain readiness insights across all five H2F domains (physical, nutritional, mental, sleep, spiritual).

The AI Engineer will play a key role in designing, developing, testing, and operationalizing machine learning (ML), AI-powered analytics, and automation models that enhance Soldier and unit readiness assessment. This role supports the Data Scientist SME, Tactical Sports Scientist, Human Performance Specialist, and the broader technical team to build responsible, explainable, and secure AI capabilities for Army use.

LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed.

Leveraging our mission-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors—helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value.

• Develop and implement ML and AI models that support Soldier readiness assessment, risk prediction, anomaly detection, and multidomain performance analytics.
• Apply supervised, unsupervised, and time-series modeling techniques to complex H2F datasets.
• Build algorithms supporting:
  • Injury risk prediction
  • Fatigue and recovery modeling
  • Training load optimization
  • Behavioral and mental readiness indicators
  • Sleep pattern analysis
  • Multidomain readiness scoring
• Ensure models are explainable, auditable, and aligned with DoD Responsible AI principles.
• Work with Data Engineers to ingest, clean, transform, and normalize data from the vendor-provided H2F data capture application.
• Ensure data structures and feature engineering pipelines support AI model performance and scalability.
• Validate data quality, missingness patterns, and wearables/sensor-derived signal integrity.
• Deploy and maintain ML models in production within Army GovCloud.
• Implement MLOps workflows including:
  • Model versioning
  • Continuous training & testing
  • Drift detection
  • Automated retraining triggers
  • Governance and documentation
• Collaborate with DevSecOps engineers to integrate models into secure CI/CD pipelines.
• Partner with the Tactical Sports Scientist and Human Performance Specialist to ensure model outputs accurately reflect human performance science and operational requirements.
• Work closely with the Data Scientist SME to validate model assumptions, metrics, and readiness indicators.
• Provide AI perspectives during Agile sprint planning, backlog refinement, and design reviews.
• Build advanced features from physiological, wellness, sleep, performance, and behavioral datasets.
• Utilize wearable and sensor data (HR, HRV, GPS, accelerometry, sleep metrics) to derive AI-ready performance indicators.
• Implement robust data preprocessing, augmentation, and normalization techniques.
• Ensure all AI implementations comply with:
  • DoD Responsible AI guidelines
  • Data privacy and security controls
  • RMF and continuous ATO requirements
  • Zero Trust principles
• Document model behavior, assumptions, risks, decision thresholds, and lineage.
• Support the UI/UX team in developing dashboards and visualizations that explain AI outputs in clear, actionable terms.
• Assist in generating decision-support products for CIMT leadership and H2F practitioners.
• Develop interpretability tools that improve user trust and understanding of AI recommendations.

Required Qualifications

• Bachelor’s degree in computer science, Data Science, Machine Learning, Artificial Intelligence, Engineering, Applied Mathematics, or a related field.
• 5+ years of experience developing and deploying machine learning or AI models in production environments.
• Experience with Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow), R, or similar tools.
• Experience building ML pipelines and working with cloud-based data and compute services (AWS, Azure, or DoD cloud environments).
• Strong understanding of ML lifecycle management, model validation, and MLOps best practices.
• Experience working with physiological, performance, or sensor-based datasets.
• Ability to obtain and maintain a DoD Secret clearance.
• Location: Remote.
• Travel: Ability to travel to Fort Eustis, VA and/or LMI HQ in Tysons, VA 1–2 times per quarter for planning and stakeholder collaboration.

Desired Qualifications

• Master’s or PhD in ML, AI, Data Science, Computer Science, or related area.
• Experience supporting H2F, CIMT, TRADOC, T2COM, or military human performance programs.
• Experience integrating ML with wearable/sensor platforms (HR/HRV, GPS, accelerometry, sleep monitors).
• Familiarity with DoD Responsible AI guidelines and RMF-aligned AI development.
• Experience with deep learning, NLP, reinforcement learning, or generative AI techniques.
• Experience contributing to dashboard tools or decision-support systems.

The target salary range for this position is $119,813.31 - $195,174.33

The salary range displayed represents the typical salary range for this position and is not a guarantee of compensation. Individual salaries are determined by various factors including, but not limited to location, internal equity, business considerations, client contract requirements, and candidate qualifications, such as education, experience, skills, and security clearances.