Sr. Artificial Intelligence Engineer (5361) (TS/SCI) (Ft. Belvoir, VA - Nolan Bldg)

SMX is seeking a Sr. Artificial Intelligence Engineer.

This is a full-time onsite position in Ft. Belvoir, VA.

Essential Duties & Responsibilities

AI Model Lifecycle & MLOps

• Design, develop, and deploy machine learning models to achieve organizational mission objectives
• Implement MLOps processes and CI/CD pipelines in containerized or reproducible computing environments to support the full ML lifecycle
• Assess and address limitations of methods to deliver machine learning models in production
• Conduct AI risk assessments to ensure models and solutions are performing as designed
• Monitor, evaluate, and optimize ML model performance using appropriate metrics

LLM Integration & Application Development

• Integrate AI solutions with cloud and enterprise IT infrastructure
• Design and implement AI-enabled applications leveraging Large Language Models (LLMs) and foundation models
• Automate development, testing, security, and deployment of AI/ML-enabled software
• Develop APIs and interfaces to enable secure, scalable interaction with AI models
• Implement Responsible AI best practices aligned with DoD AI Ethical Principles

Technical Leadership & Collaboration

• Mentor and provide technical guidance to junior AI/ML engineers and data scientists.
• Serve as the technical lead for AI solution architecture, making final determinations on model selection and deployment frameworks.
• Analyze ML model outputs and translate results for technical and non-technical stakeholders
• Explain AI concepts and terminology clearly to cross-functional teams
• Identify low-probability, high-impact risks in ML training data and throughout the AI solution lifespan
• Research and evaluate the latest ML and AI tools, techniques, and best practices
• Write and document reproducible, secure code with proper error handling

Mission Support

• Collaborate with stakeholders to address data privacy, PII, PHI, and data reusability concerns
• Ensure AI design and development activities are properly documented and updated
• Conduct hypothesis testing using statistical processes
• Use knowledge of business processes to create or recommend AI solutions

Required Skills, Experience & Education

Security

• Active TS security clearance and eligible for SCI and NATO read-on prior to starting work
• Meet all requirements to receive a privileged user account on a TS/SCI information system (e.g. Army Cloud Computing Service Provider) prior to starting work. The requirements are currently defined in DoDD 8140.01.
• Security+ or related DoDD 8140-relevant certification (or ability to obtain within 6 months of hire)

Education and Experience

• Master’s degree in Computer Science, Data Science, Software Engineering, Mathematics or Statistics, Computer Engineering, Information Technology or related field and 3+ years of experience in AI/ML engineering, with demonstrable expertise in model deployment and operationalization, or
• Bachelor's degree in Computer Science, Data Science, Software Engineering, Mathematics or Statistics, Computer Engineering, Information Technology or related field and 5+ years of experience in AI/ML engineering, with demonstrable expertise in model deployment and operationalization
• Hands-on experience with MLOps processes, CI/CD for ML, and containerized deployment environments (Docker, Kubernetes)
• Knowledge of Responsible AI frameworks and bias mitigation techniques

Technical Skills

• Strong proficiency in machine learning theory, model development, and deployment
• Experience integrating AI solutions with LLMs (e.g., OpenAI GPT, Azure OpenAI, AWS Bedrock, or open-source alternatives)
• Proficiency in Python scripting and ML frameworks (TensorFlow, PyTorch, scikit-learn, Hugging Face)
• Knowledge of cloud platforms (AWS, Azure, GCP) and AI/ML service models (SaaS, IaaS, PaaS)
• Understanding of AI security risks, threats, and vulnerabilities, and mitigation strategies
• Familiarity with testing, evaluation, validation, and verification (T&E V&V) for AI systems

Analytical & Communication Skills

• Ability to evaluate ML model effectiveness using appropriate metrics
• Skill in identifying and mitigating risks across the AI lifecycle
• Strong technical writing and presentation skills
• Ability to tailor technical information to diverse audiences

Professional Attributes:

• Judgment – Assessing trade-offs and making informed technical