Description

You will build, tune, and deploy machine learning models into production environments.

This role is hybrid.

Responsibilities

• Build, tune, and deploy machine learning models in production environments.
• Implement MLOps practices for model versioning, deployment, and governance using MLflow.
• Design and integrate AI architectures using microservices into operational platforms.
• Manage containerization workflows for scalable inference serving using Docker and Kubernetes.
• Apply explainability frameworks (SHAP, LIME) and bias detection techniques to AI systems.

Required Skills

• 4+ years of experience building and deploying ML models in production.
• Proficiency in MLOps practices, specifically with MLflow or similar tooling.
• Experience designing microservices-based AI architectures.
• Expertise in containerization, including Docker and Kubernetes.
• Knowledge of model explainability (SHAP, LIME) and AI bias detection.
• Minimum of 5+ years of professional experience.
• BS in Engineering, Computer Science, Information Systems, or equivalent.

Preferred Skills

• Experience deploying AI models in regulated mission environments (healthcare, federal security, customs).
• Familiarity with graph transformers or hybrid rule+AI architectures.

Key Skills

Education

Any Graduate