onsite
MLOps Engineer - Artificial Intelligence
MLOps Engineer
Drive end‑to‑end AI model deployment as an MLOps Engineer, building scalable CI/CD pipelines with Docker, Kubernetes, and Git on Linux environments to accelerate AI product delivery.
About the role
Key Responsibilities
- Design, implement, and maintain CI/CD pipelines for AI model training, testing, and deployment.
- Containerize ML workloads using Docker and orchestrate them with Kubernetes on Linux clusters.
- Integrate version control (Git) and artifact repositories to ensure reproducible model builds.
- Collaborate with data scientists and software engineers to streamline model lifecycle management.
- Monitor and troubleshoot production ML services, ensuring high availability and performance.
Requirements
- Proven experience with MLOps practices and tools.
- Strong knowledge of Docker, Kubernetes, and Linux system administration.
- Hands‑on experience with CI/CD tools (e.g., Jenkins, GitLab CI, Argo CD).
- Familiarity with AI/ML frameworks and model serving.
- Excellent problem‑solving skills and a collaborative mindset.
Skills
mlopscicddockerkuberneteslinux