Machine Learning Engineer – AI Model Developer, Mid to Senior

• Design, develop, train, evaluate, and deploy machine learning models supporting mission objectives.
• Collaborate with Government stakeholders to define AI/ML problems, success metrics, and evaluation criteria.
• Develop Model Selection Briefs documenting model performance, trade studies, reproducibility artifacts, and technical recommendations.
• Integrate Government-provided human baseline results into model evaluation and performance analysis.
• Design and coordinate Human-in-the-Loop (HITL) baseline experiments when baseline data does not exist.
• Support implementation and operational integration of production-ready machine learning models.
• Collaborate with data engineers to prepare, validate, and optimize datasets for AI/ML workloads.
• Develop backend services and APIs supporting AI-enabled applications.
• Support cloud-native model development, deployment, and lifecycle management within secure environments.
• Participate in architecture discussions, code reviews, testing, documentation, and quality assurance activities.
• Work closely with Government customers and Agile development teams to deliver innovative AI solutions that meet mission requirements.

• Active Top Secret Security Clearance

Demonstrates expertise in designing, developing, and deploying machine learning models while collaborating with stakeholders to define AI/ML problems and success metrics. Proficient in integrating human baseline results and supporting cloud-native model development within secure environments.

• Machine Learning Model Development
• AI/ML Problem Definition
• Human-in-the-Loop Experiment Design
• Cloud-Native Model Deployment
• Active Top Secret Security Clearance

• Machine Learning
• Model Evaluation
• Data Preparation
• Backend Services Development
• API Development
• Model Selection Briefs
• Performance Analysis
• Agile Development
• Technical Recommendations
• Quality Assurance

• Collaboration
• Communication

• Top Secret Security Clearance

• Human Baseline Results
• HITL Experiments
• Data Validation
• Dataset Optimization
• Mission Objectives

• Cloud Environments
• AI-Enabled Applications