Senior ML Platform & MLOps Engineer

Remote Worker LTD.; Greater London; On-site; GBP 110,000 - 150,000; Full time

Benefits offered by this job

Health insurance

Learning & Development budget

Time off for self-development

Job summary

Preply is seeking a Senior ML Platform/Ops Engineer to productionize ML systems with reliability, performance and observability. You’ll bridge ML, data engineering and cloud infrastructure to enable fast, secure model development from training to deployment.

You’ll collaborate with ML Scientists, Backend Engineers and Data Engineers to shape the foundations of the ML lifecycle and scale model-driven features across the platform.

Qualifications

• 5+ years designing and deploying ML systems in production.
• Proficiency in Python and SQL, and orchestration tools (Airflow, Kubeflow, Dagster).
• Experience with cloud platforms (GCP or AWS), Kubernetes, and CI/CD workflows.
• Understanding of ML lifecycles: training, validation, deployment, monitoring.
• Strong DevOps: Git, IaC (Terraform), logging/observability, containers.

Responsibilities

• Build and maintain ML pipelines for training, evaluation, and deployment.
• Define observability and alerting for ML systems (drift, data quality, feature coverage).
• Collaborate with ML Scientists, Backend and Data Engineers.
• Scale data ingestion and feature transformation (batch and streaming).
• Contribute to internal Python libraries and platform tooling.
• Ensure ML services are modular, testable, and monitored from day one.
• Exploration and productionization of LLM-based features.

Skills

Python

SQL

Airflow

Kubeflow

Dagster

Databricks

MLFlow

Sagemaker

Kubernetes

CI/CD

Terraform

Docker

Observability

Tools

Databricks

MLFlow

Airflow

DBT

Sagemaker

Tecton

Job description

Preply is seeking a Senior ML Platform/Ops Engineer to productionize ML systems with reliability, performance and observability. You’ll bridge ML, data engineering and cloud infrastructure to enable fast, secure model development from training to deployment.

You’ll collaborate with ML Scientists, Backend Engineers and Data Engineers to shape the foundations of the ML lifecycle and scale model-driven features across the platform.