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
Use knowledge of business processes to create or recommend AI solutions
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
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
Experience with DoD AI Ethical Principles (responsible, equitable, traceable, reliable, governable)
Familiarity with NIST Risk Management Framework (RMF) or cybersecurity compliance standards
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