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

SMX•Fort Belvoir, VA

4d•$165,000 - $180,000•Onsite

About The Position

SMX is seeking a Sr. Artificial Intelligence Engineer. This is a full-time onsite position in Ft. Belvoir, VA. The role involves designing, developing, and deploying machine learning models, implementing MLOps processes, integrating AI solutions with cloud infrastructure, and developing AI-enabled applications leveraging Large Language Models (LLMs). The engineer will also provide technical leadership, mentor junior staff, and collaborate with stakeholders to ensure AI solutions meet mission objectives while adhering to Responsible AI best practices and security requirements.

Requirements

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).
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.
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.
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.
Judgment – Assessing trade-offs and making informed technical decisions.
Problem-solving – Framing complex challenges and developing actionable solutions.
Execution orientation – Delivering results in dynamic, fast-paced environments.
Innovation & creativity – Recommending improvements and exploring emerging AI capabilities.
Risk-centered mindset – Understanding threats, vulnerabilities, and mission impacts.
Trustworthiness – Operating with integrity in highly sensitive environments.

Nice To Haves

Experience with DoD AI Ethical Principles (responsible, equitable, traceable, reliable, governable).
Familiarity with NIST Risk Management Framework (RMF) or cybersecurity compliance standards.
Experience in defense or IC AI/ML projects.
Relevant certifications (e.g., AWS Certified Machine Learning, Azure AI Engineer, TensorFlow Developer).

Responsibilities

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.
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.
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.
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.