Note: The job is a remote job and is open to candidates in USA. LMI is a digital solutions provider delivering technology and mission-ready AI solutions to federal agencies. The AI/ML Engineer will design, deploy, optimize, integrate, and sustain production AI/ML capabilities, including predictive forecasting, natural language processing, generative AI, and real-time decision support, within secure applications and enterprise workflows.
Responsibilities
- 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
Skills
- 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
- 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
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