AI Engineer

About the Role

As the AI Engineer, you will demonstrate proficiency using AI tools for coding, debugging, refactoring, and documentation. You will have strong programming skills in Python or JavaScript/TypeScript, familiarity with LLM concepts (tokens, embeddings, transformers) and modern ML workflows, experience with Git and modern DevOps tools, and strong problem-solving, analytical reasoning, and communication skills. You understand how to leverage modern AI tools—including Claude, ChatGPT, and other advanced coding assistants—to dramatically increase software development productivity, develop AI-enhanced applications, integrate large language models (LLMs) into workflows, and accelerate delivery through AI-driven coding, analysis, and automation. You will support mission-aligned projects across healthcare, federal agencies, and regulated environments.

Task Areas

Task Areas that this position will support include Scaling AI Capabilities across the Enterprise. Support may include, but is not limited to:

Infrastructure Design

• Current Infrastructure Assessment: Evaluate existing AI/ML platforms, cloud infrastructure, data storage capabilities, and computational resources across FDA's enterprise environment.
• Scalability Analysis: Assess current system capacity to handle increased AI workloads, identify bottlenecks, and develop capacity planning models for future growth.
• Architecture Design: Create detailed technical specifications for enhanced AI infrastructure, including cloud-native solutions, containerization strategies, and microservices architecture.
• Security and Compliance Framework: Support and determine how Client can use established AI tools to comply with federal cybersecurity requirements, including proper access controls, data handling protocols, and ongoing compliance monitoring.

Solution Development

• AI Model Selection and Validation: Support and determine which existing AI models are best suited for specific regulatory workflows such as document review automation, inspection prioritization, and clinical trial optimization, and how do we validate their performance for FDA use cases.
• System Integration: Assess and determine how Client can effectively integrate established AI solutions with existing FDA systems (FAERS, Orange Book, NDC Directory, etc.) to ensure seamless data flow and operational compatibility.
• Performance Monitoring Systems: Implement real-time monitoring dashboards for model performance, data drift detection, and automated alerting systems for performance degradation.
• User Adoption and Training: Determine how to design effective training programs and support systems to help FDA staff successfully adopt and utilize AI tools in their daily regulatory work.

Governance Framework

• Model Lifecycle Management: Establish comprehensive protocols for model development, validation, deployment, monitoring, and retirement across the AI model lifecycle.
• Quality Assurance Standards: Develop testing protocols, validation criteria, and quality gates for AI model deployment in regulatory environments.
• Change Management Procedures: Create formal processes for model updates, retraining schedules, and version control management.
• Risk Management Framework: Implement risk assessment protocols for AI deployment, including bias detection, fairness evaluation, and regulatory impact.

Qualifications

• Bachelors Degree in Engineering or related field
• 3-5 years of experience
• Must be able to obtain and maintain DoD clearance