Job description

LMI is seeking an Artificial Intelligence and Machine Learning (AI/ML) Engineer to support a Special Operations Command (SOCOM) mission partner with production machine learning, predictive forecasting, natural language processing, generative AI, and real-time decision-support capabilities. The AI/ML Engineer will design, implement, optimize, integrate, and sustain scalable AI/ML solutions within secure web-based applications and enterprise workflows. This position will work as part of a cross-functional data science product team to translate validated models into reliable operational capabilities while advancing reusable engineering patterns, governance, security, documentation, and enterprise AI/ML best practices. 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 achieve mission success. This position requires an active Secret security clearance with the ability to obtain a Top Secret clearance. • Design, implement, test, and optimize machine learning algorithms for predictive forecasting, rate prediction, resource planning, and real-time decision support. • Develop and refine supervised, unsupervised, time-series, regression, ensemble, and other appropriate models to meet stringent accuracy, reliability, explainability, latency, and efficiency requirements. • Develop natural language processing and generative AI solutions, including large language models and retrieval-augmented generation capabilities tailored to approved business, operational, and intelligence use cases. • Engineer reusable model services, application programming interfaces, containers, and software components that integrate seamlessly into secure web-based applications, dashboards, and other mission data products. • Design scalable and reliable architectures for batch and real-time inference, model serving, monitoring, and application support across development, test, and production environments. • Integrate predictive models into existing web-based applications and enterprise workflows while ensuring compatibility, reliability, security, and seamless user functionality. • Collaborate with data scientists and data engineers to establish compatible data structures, features, pipelines, interfaces, and validation methods for model training, evaluation, deployment, and sustainment. • Conduct performance testing, hyperparameter tuning, error analysis, back-testing, drift detection, and model monitoring; document assumptions, limitations, risks, and opportunities for continued improvement. • Implement MLOps and DevSecOps practices for source control, automated testing, continuous integration and delivery, model versioning, deployment, monitoring, rollback, and repeatable sustainment. • Apply responsible and secure AI/ML engineering practices, including access control, data protection, model governance, explainability, evaluation, auditability, and risk management. • Support enterprise synchronization, integration, governance, sustainment, and adoption of AI/ML capabilities across multiple mission teams, stakeholder organizations, applications, and products. • Develop projects that automate or augment human cognitive workload and respond rapidly to emerging operational requirements and changes in the mission environment. • Produce and maintain technical documentation covering algorithms, system architecture, interfaces, security, testing, deployment, operations, and integration processes. • Develop user guides, training materials, demonstrations, instructional videos, and knowledge-transfer products sufficient for a qualified practitioner to assume future operation and sustainment of the capability. • Provide rapid-response engineering and product-level staff augmentation based on changes in mission priorities and the operational environment. Required Qualifications • Active Secret security clearance with the ability to obtain a Top Secret clearance. • Bachelor’s degree in computer science, artificial intelligence, machine learning, data science, software engineering, mathematics, engineering, or a related technical field. • Five or more years of professional experience designing, developing, deploying, and sustaining machine learning models or AI-enabled software capabilities in production environments. • Advanced proficiency with Python and practical experience with modern machine learning frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, XGBoost, or comparable technologies. • Demonstrated experience developing and validating predictive models, including time-series, regression, ensemble, or comparable forecasting methods, against defined accuracy and performance requirements. • Experience operationalizing models through application programming interfaces, services, containers, automated testing, version control, continuous integration and delivery, model registries, monitoring, and repeatable deployment processes. • Working knowledge of SQL, data structures, feature pipelines, data quality controls, and secure integration with relational, non-relational, object-storage, or analytical data platforms. • Experience developing scalable architectures for batch or real-time inference, model serving, application integration, monitoring, and production support. • Knowledge of responsible and secure AI/ML engineering practices, including access control, data protection, model governance, explainability, evaluation, auditability, and risk management. • Experience producing clear algorithm, architecture, interface, test, deployment, operational, and knowledge-transfer documentation for technical and non-technical stakeholders. • Strong written and verbal communication skills and the ability to collaborate across data science, data engineering, software, cybersecurity, governance, and operational teams. • Ability to independently manage multiple priorities and deliver reliable production capabilities in a fast-paced, mission-focused environment. Preferred Qualifications • Master’s degree in computer science, artificial intelligence, machine learning, data science, software engineering, mathematics, engineering, or a related technical field. • Experience with secure government cloud environments such as AWS GovCloud or Azure Government and with Kubernetes, infrastructure as code, DevSecOps, or comparable production delivery practices. • Prior military service or direct professional experience supporting U.S. Special Operations Forces. Target Competencies • Mission Focus • Technical Excellence • Systems Engineering • Product Ownership • Collaboration and Stakeholder Engagement • Clear Communication • Adaptability and Continuous Learning Target Salary Range: $122,000-$211,000 Disclaimer: 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. US-Remote We can recommend jobs specifically for you! Click here to get started.

Requirements

Must-haves

This position requires an active Secret security clearance with the ability to obtain a Top Secret clearance.
Design, implement, test, and optimize machine learning algorithms for predictive forecasting, rate prediction, resource planning, and real-time decision support.
Develop and refine supervised, unsupervised, time-series, regression, ensemble, and other appropriate models to meet stringent accuracy, reliability, explainability, latency, and efficiency requirements.
Develop natural language processing and generative AI solutions, including large language models and retrieval-augmented generation capabilities tailored to approved business, operational, and intelligence use cases.
Engineer reusable model services, application programming interfaces, containers, and software components that integrate seamlessly into secure web-based applications, dashboards, and other mission data products.
Design scalable and reliable architectures for batch and real-time inference, model serving, monitoring, and application support across development, test, and production environments.
Integrate predictive models into existing web-based applications and enterprise workflows while ensuring compatibility, reliability, security, and seamless user functionality.
Collaborate with data scientists and data engineers to establish compatible data structures, features, pipelines, interfaces, and validation methods for model training, evaluation, deployment, and sustainment.
Conduct performance testing, hyperparameter tuning, error analysis, back-testing, drift detection, and model monitoring; document assumptions, limitations, risks, and opportunities for continued improvement.
Implement MLOps and DevSecOps practices for source control, automated testing, continuous integration and delivery, model versioning, deployment, monitoring, rollback, and repeatable sustainment.
Apply responsible and secure AI/ML engineering practices, including access control, data protection, model governance, explainability, evaluation, auditability, and risk management.
Support enterprise synchronization, integration, governance, sustainment, and adoption of AI/ML capabilities across multiple mission teams, stakeholder organizations, applications, and products.

Nice-to-haves

Master’s degree in computer science, artificial intelligence, machine learning, data science, software engineering, mathematics, engineering, or a related technical field.
Experience with secure government cloud environments such as AWS GovCloud or Azure Government and with Kubernetes, infrastructure as code, DevSecOps, or comparable production delivery practices.
Prior military service or direct professional experience supporting U.S. Special Operations Forces.
Target Competencies
Mission Focus
Technical Excellence
Systems Engineering
Product Ownership
Collaboration and Stakeholder Engagement
Clear Communication
Adaptability and Continuous Learning
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Keywords to mirror

machine learningaimlpythonsqlawsazurekubernetes

Platform: icims
Effort: medium