Senior ML Platform & Ops Engineer - Hybrid (London)

Preply; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Monthly lesson allowance

Learning & Development budget

Health insurance

Mental health support access

Equity

Job summary

Preply is hiring a Senior ML Platform/Ops Engineer in London. You will help productionize ML systems with reliability, performance, and observability, working at the intersection of ML, data engineering, and cloud infrastructure.

You’ll collaborate with ML Scientists, Backend and Data Engineers to shape the ML lifecycle foundations. Expect responsibilities across training, deployment, monitoring, and scalable data pipelines.

Qualifications

• 5+ years designing and deploying ML systems in production.
• Proficiency in Python and SQL; experience with orchestration tools.
• Experience on cloud platforms (GCP/AWS) and Kubernetes; CI/CD workflows.
• Understanding of ML model lifecycles: training, validation, deployment, monitoring.
• Ability to mentor peers and collaborate with ML Scientists.
• Exposure to LLM serving and GenAI-powered product flows.

Responsibilities

• Build and maintain ML pipelines for training, evaluation, and deployment.
• Develop reproducible training environments and scale compute.
• Define observability and alerting for ML systems (drift, data quality).
• Design data ingestion and feature transformation for batch/streaming.
• Contribute to internal libraries and platform tooling for rapid experimentation.
• Ensure ML services are modular, testable, and monitored from day one.
• Explore and productionize LLM-based features and retrieval pipelines.

Skills

Python

SQL

Airflow

Kubeflow

Dagster

CI/CD

Kubernetes

LLM serving

Vector databases

GenAI SDLC

ML lifecycles

Tools

Databricks

MLFlow

Airflow

Sagemaker

Job description

Preply is hiring a Senior ML Platform/Ops Engineer in London. You will help productionize ML systems with reliability, performance, and observability, working at the intersection of ML, data engineering, and cloud infrastructure.

You’ll collaborate with ML Scientists, Backend and Data Engineers to shape the ML lifecycle foundations. Expect responsibilities across training, deployment, monitoring, and scalable data pipelines.