Description

• Design, develop, and optimize AI/ML models to support predictive analytics, automation, and decision‑support functions.
• Build, maintain, and enhance data pipelines that support model training, tuning, validation, and deployment.
• Train, evaluate, and refine algorithms using modern ML frameworks, ensuring accuracy, reliability, and performance.
• Deploy AI/ML models into production environments, integrating with enterprise systems, APIs, and cloud platforms.
• Identify, evaluate, and integrate appropriate COTS, government, and custom AI/ML tools within established enterprise frameworks.
• Ensure all AI/ML solutions meet security, compliance, and performance requirements, including alignment with enterprise architecture standards.
• Collaborate with data engineers, cloud engineers, DevSecOps teams, and mission stakeholders to deliver scalable, maintainable AI/ML capabilities.
• Support model monitoring, drift detection, retraining workflows, and continuous improvement cycles.
• Document and communicate technical approaches, model behavior, and analytical insights to both technical and non‑technical audiences.
• Contribute to modernization initiatives by recommending new methodologies, tools, and best practices in AI/ML engineering.

What You’ll Need:

• Experience with Python, TensorFlow, PyTorch, Scikit‑learn, or similar ML frameworks.
• Hands‑on experience with cloud‑based AI/ML services (AWS Sagemaker, Azure ML, OCI Data Science).
• Strong understanding of data engineering concepts, including ETL/ELT, feature stores, and distributed processing.
• Familiarity with MLOps practices, CI/CD pipelines, and automated model deployment workflows.
• Knowledge of security and compliance requirements for government or regulated environments.
• Ability to translate complex analytical concepts into clear, actionable insights

Key Skills

Education

Bachelor's degree