Artificial Intelligence Subject Matter Expert

The Role

What Success Looks Like

• Model performance gets validated against real operational conditions, not only against a clean benchmark dataset.
• Where a model is wrong, you can say how it is wrong and how often, rather than reporting a single accuracy number and stopping there.
• You can explain a model's behavior to someone who did not build it, in terms they can evaluate.

• Model behavior gets monitored after deployment, not just validated once before it.
• Drift and degraded performance get caught before they change an operational outcome.
• Failure modes are documented and understood before a model goes live, not discovered afterward.

• Documentation and evidence for a security or accreditation review come out of your normal development process.
• You can tell an engineering team early what an AI capability will need to clear review, before they have built around an assumption that will not survive it.
• Reviewers get a straight answer about what a model does and does not do.

• Other engineers and program staff come to you before committing to an AI approach, not after it has already been built.
• Where AI is not the right tool for a problem, you can say so and be heard.
• Standards for evaluating and deploying models exist and get used on the next project, not reinvented each time.

What You Bring

• 5+ years in ML, Data Science, or AI engineering
• 2+ years focused on Generative AI and LLMs
• Deep expertise in machine learning, deep learning, and statistical modeling 
• Hands-on experience with LLM frameworks, vector databases, and prompt engineering
• Advanced Python proficiency; production-grade software development experience
• MLOps and cloud AI platform experience (Azure AI, AWS Bedrock, Google Vertex AI)
• Proven track record taking AI solutions from PoC to production
• Experience in assessing cyber risk and functionality of AI solutions
• Experience in incorporating automation and artificial intelligence into cyber prpgrams
• Experience with data analytic software such as Splunk, Cribl, Elastic and Sentinel

• Open-source contributions or published AI research
• Experience with edge/low-latency model optimization
• Certifications in AI Ethics, Responsible AI, or Cloud AI Architecture
• Familiarity with adversarial AI testing and red-teaming
• You have taken a machine learning model from prototype to a production system that real people depended on.
• You have worked in a regulated or federal environment where a model's decisions had to be explainable and defensible, not only accurate.
• You have caught a model failure before it reached a decision that mattered, and can describe how.
• You are comfortable across the model lifecycle: data, training, validation, deployment, and monitoring, rather than specialized in only one stage.
• You hold an active CBP BI, a fitness determination at another DHS component, or an active DoD clearance. Any of these shortens your start date.
• You can talk about the limits of a model as clearly as you talk about its capabilities.

A note on timing

Employee Benefits

• Happy - Be Infectious. Happiness multiplies and creates a positive and connected environment where motivation and satisfaction have an outsized effect on everything we do.
• Helpful - Be Supportive. Being helpful is the foundation of teamwork, resulting in a supportive atmosphere where collaboration flourishes, and collective success is celebrated.
• Honest - Be Trustworthy. Honesty serves as our compass, ensuring transparent communication and ethical conduct, essential to who we are and the complex domains we support.
• Humble - Be Grounded. Success is not achieved alone, humility ensures a culture of mutual respect, encouraging open communication, and a willingness to learn from one another and take on any task.
• Hungry - Be Eager. Our hunger for excellence drives an insatiable appetite for innovation and continuous improvement, propelling us forward in the face of new and unprecedented challenges.
• Hustle - Be Driven. Hustle is reflected in our relentless work ethic, where we are each committed to going above and beyond to advance the mission and achieve success.