onsite
ML Platform Engineer - synthesia
Devops Engineer
Design and operate scalable machine‑learning infrastructure, enabling rapid model training, deployment, and monitoring on cloud platforms using Python, TensorFlow/PyTorch, Kubernetes, and CI/CD pipelines.
About the role
Key Responsibilities
- Architect, build, and maintain a robust ML platform that supports end‑to‑end model lifecycle management.
- Develop reusable pipelines for data ingestion, feature engineering, model training, and automated deployment.
- Implement containerized workloads with Docker and orchestrate them on Kubernetes clusters in AWS.
- Ensure high availability, scalability, and security of the platform through monitoring, logging, and automated testing.
- Collaborate with data scientists and software engineers to optimize model performance and reduce time‑to‑production.
Requirements
- 5+ years of experience building ML infrastructure or platform engineering.
- Strong proficiency in Python and deep‑learning frameworks such as TensorFlow or PyTorch.
- Hands‑on experience with Kubernetes, Docker, and cloud services (AWS preferred).
- Expertise in CI/CD tools and infrastructure‑as‑code practices.
- Solid understanding of distributed systems, networking, and security best practices.
Skills
pythontensorflowpytorchkubernetesawsdockercicd