AI/ML Engineer

We are seeking a skilled AI/ML MLOps Engineer to support the deployment, monitoring, and maintenance of machine learning models in secure environments. This role focuses on operationalizing AI/ML workflows using MLOps best practices, with an emphasis on lifecycle management, observability, scalability, security, and incident response within mission-critical environments. The ideal candidate will have experience with cloud infrastructure, CI/CD pipelines, containerization, and secure DevSecOps environments.

Design, implement, manage, and maintain scalable MLOps pipelines for deploying machine learning models into production.
• Automate testing, validation, and monitoring of AI/ML models to ensure high performance and reliability.
• Manage infrastructure using Infrastructure-as-Code (IaC) tools for secure and scalable deployments.
• Collaborate with data scientists and machine learning engineers to integrate models into end-user applications.
• Ensure compliance with security protocols for handling and processing classified data.
• Monitor model drift, performance degradation, and initiate retraining workflows as needed.
• Implement model versioning and experiment tracking to ensure reproducibility and traceability.
• Implement CI/CD pipelines to streamline development and deployment cycles.
• Document workflows, procedures, and infrastructure for reproducibility and auditability.
• Integrate and manage data ingestion and preprocessing pipelines aligned with secure data governance practices.

Degree in computer science, engineering, mathematics, or related field. A minimum of 8 years of related experience with a Bachelor's degree, 6 years with a Master's degree, 3 years with a PhD, or equivalent experience.

• 3+ years of experience in MLOps or DevOps roles supporting AI/ML deployments.
• Experience in conducting Test & Evaluation including evaluating and documenting the AI models and data across performance dimensions, informed by system and mission constraints.
• Experience with software development, software accreditation, software development best practices, code hygiene tools, and documentation.
• Proficiency in Python, Bash, and scripting languages for automation.
• Familiarity with ML frameworks such as TensorFlow, PyTorch, Keras, and ONNX.
• Familiarity with observability tools such as Prometheus/Grafana, ELK Stack, OpenTelemetry, and Fiddler.
• Strong understanding of and hands-on experience with cloud platforms (AWS, Azure, GCP), container orchestration (e.g., Kubernetes, Docker), containerized pipelines and toolkits (e.g. Kubeflow), and Infrastructure-as-Code tools (Terraform, CloudFormation).
• Experience with CI/CD tools (GitLab CI, Jenkins, etc.) and MLOps platforms.
• Experience working in secure environments and handling classified data.
• Active TS/SCI clearance.