onsite
Senior MLOps Engineer - IMPACT
MLOps Engineer
Lead the design and standardization of scalable deployment pipelines for machine‑learning models, integrating platforms such as Kubeflow, KServe, and Orbital Pipelines, while driving best practices for model serving, monitoring, and health checks.
About the role
Key Responsibilities
- Design, develop, and standardize deployment solutions for machine‑learning models across the organization.
- Build and maintain scalable deployment pipelines using Kubeflow, KServe, and Orbital Pipelines.
- Develop wrappers, SDK extensions, and configuration templates for various ML frameworks.
- Define and enforce best practices for model serving, monitoring, and health checks.
- Produce comprehensive technical documentation and conduct knowledge‑transfer sessions.
Requirements
- Extensive experience in MLOps, with hands‑on work in Kubeflow, KServe, and Orbital Pipelines.
- Strong programming skills in Python and familiarity with Docker and Kubernetes.
- Proven ability to create reusable, scalable deployment architectures.
- Excellent communication skills for documentation and cross‑team collaboration.
Skills
mlopspythondockerkubernetes