onsite
MLOps Engineer SC Clearance - CGI
MLOps Engineer
Design, build, and operate secure, scalable machine‑learning platforms for national‑security projects, leveraging Python, Docker, Kubernetes, AWS, CI/CD pipelines, and infrastructure‑as‑code tools.
About the role
Key Responsibilities
- Architect and implement end‑to‑end MLOps pipelines that automate model training, validation, and deployment in secure cloud environments.
- Containerize machine‑learning workloads using Docker and orchestrate them with Kubernetes, ensuring high availability and compliance with security standards.
- Develop and maintain CI/CD workflows (GitLab, Jenkins, or Azure DevOps) to streamline code integration, testing, and release cycles.
- Manage cloud infrastructure on AWS, employing Terraform for reproducible, version‑controlled provisioning.
- Collaborate with data scientists, security engineers, and defence stakeholders to embed security controls, monitoring, and auditability into AI systems.
- Provide operational support, troubleshooting, and performance tuning for production ML services.
Requirements
- Strong experience with Python for data processing and model integration.
- Proficiency in container technologies (Docker) and orchestration platforms (Kubernetes).
- Hands‑on expertise in cloud platforms, preferably AWS, and infrastructure‑as‑code tools such as Terraform.
- Demonstrated ability to design CI/CD pipelines for machine‑learning workflows.
- Security clearance (SC) and familiarity with defence‑grade compliance frameworks.
Skills
pythondockerkubernetesawscicdterraform