Requirements

Expected technologies

Kubernetes

Docker

Airflow

Kubeflow

MLflow

SageMaker

Vertex AI

Python

Terraform

HELM

Optional technologies

Prometheus

Grafana

Our requirements

• Proven experience in MLOps, DevOps for AI, or ML platform engineering.
• Proficiency in Kubernetes, Docker, and workflow orchestration tools.
• Strong programming skills in Python.
• Hands-on experience with Infrastructure-as-Code tools (e.g., Terraform, Helm).
• Production-grade ML model deployment experience using AWS SageMaker, Azure ML, or Google Vertex AI.
• Understanding of data pipelines, feature stores, and cloud architectures.
• Proficiency in CI/CD for ML, including versioning, testing, and secure deployment.
• Strong teamwork, problem-solving, and communication skills.

Optional

• Experience with monitoring tools such as Prometheus and Grafana.
• Familiarity with ML observability platforms.
• Knowledge of compliance, governance, and model risk management in regulated environments.

Your responsibilities

• Design, build, and scale MLOps pipelines for training, evaluation, versioning, and deploying ML models.
• Manage containerized environments using Kubernetes and Docker.
• Orchestrate workflows with Airflow, MLflow, Kubeflow, or similar tools.
• Deploy and monitor ML models on AWS SageMaker, Azure ML, or Google Vertex AI.
• Implement CI/CD pipelines with a strong focus on security, reproducibility, and reliability.
• Collaborate with Data Science, ML, and Infrastructure teams for seamless model handover.
• Implement ML observability practices: model drift detection, latency monitoring, and performance metrics tracking.
• Support governance, feature store integration, and reproducibility standards within the organization.