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# Senior Machine Learning Platform/Ops Engineer

- **Company:** Preply Inc.
- **Location:** London, UK
- **Experience:** Expert
- **Salary:** £79,589.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, BigQuery, Continuous Integration, DevOps, Python (Programming Language), Machine Learning, Systems Development Life Cycle, Azure Machine Learning, SQL Databases, Workflow Management Systems, Data Logging, Data Ingestion, Large Language Models, Apache Spark, Backend, Git, Containerization, Kubernetes, Apache Kafka, Machine Learning Operations, Terraform, Docker, Databricks
- **Published:** September 25, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5896924066

## About the Role

* Proven experience designing and deploying ML systems in production (5+ years in relevant roles)
 * Proficiency in Python and SQL, and orchestration tools (Airflow, Kubeflow, Dagster, etc.)
 * Experience with modern cloud platforms (preferably GCP or AWS), Kubernetes, and CI/CD workflows
 * Understanding of ML model lifecycles: training, validation, deployment, and monitoring
 * Strong DevOps practices: Git, IaC (Terraform), logging/observability, containerization (Docker/K8s)
 * Ability to work independently with ML Scientists and mentor peers in reliability, testing, and delivery. Product impact driven.
 * Exposure to LLM serving, vector databases, or GenAI-powered product flows
 * Deep, hands-on expertise in AI tools, especially in agentic AI SDLC

## Description

You'll collaborate closely with ML Scientists, Backend Engineers, and Data Engineers to shape the foundations of our ML lifecycle.

 * Build and maintain ML pipelines for training, evaluation, and deployment using tools like Databricks, MLFlow, Airflow, DBT, Sagemaker, Tecton
 * Support AI scientist creating reproducible, containerized model training environments (on-demand and scheduled), and manage compute at scale (e.g., spot/GPU autoscaling)
 * Define and implement observability and alerting for ML systems (model drift, data quality, feature coverage, etc.)
 * Design and scale data ingestion and feature transformation flows using batch (e.g., Spark/BigQuery) and streaming (Kafka or equivalent)
 * Contribute to internal Python libraries and platform tooling that accelerate experimentation and deployment for all model teams
 * Ensure ML services are modular, testable, and monitored from day one
 * Exploration and productionization of LLM-based features (e.g., retrieval pipelines, prompt evaluation, model serving)


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