MLOps & DevOps Engineer

About the role

We are looking for a highly skilled MLOps & DevOps Engineer to bridge data‑science research and AI engineering, ensuring smooth delivery of AI platforms and Retrieval‑Augmented‑Generation pipelines at scale.

Key responsibilities

• Design, provision and maintain scalable cloud infrastructure on Google Cloud Platform (GCP) using Terraform, focusing on GKE clusters, firewalls and network policies.
• Develop and operate end‑to‑end CI/CD pipelines with GitHub Actions, integrating Renovate for dependency updates, Sonar for code quality checks and Artifactory for binary management.
• Build, automate and monitor machine‑learning pipelines on Vertex AI, including RAG architectures and automated data ingestion into Qdrant vector databases.
• Implement evaluation and monitoring pipelines to continuously measure model performance and cost‑efficiency.
• Optimize system performance, enforce security best practices and drive cost‑saving initiatives across cloud environments.

Required profile

• Strong background in cloud infrastructure engineering and DevOps practices.
• Hands‑on experience with the full software development lifecycle for AI/ML solutions.
• Ability to work autonomously and collaborate with data‑science and engineering teams.

Required skills

• Google Cloud Platform (GCP)
• Terraform
• Google Kubernetes Engine (GKE)
• GitHub Actions
• Renovate
• SonarQube (Sonar)
• Artifactory
• Vertex AI
• Retrieval‑Augmented‑Generation (RAG) pipelines
• Qdrant vector database