Senior Machine Learning Platform/Ops Engineer

We power people’s progress.

At Preply, we’re all about creating life-changing learning experiences. We help people discover the magic of the perfect tutor, craft a personalised learning journey, and stay motivated to keep growing. Our approach is human-led, tech-enabled - and it’s creating real impact.

We’ve just reached unicorn status with a $150M Series D, accelerating our vision to transform education through human-led, AI-enhanced learning. Today, 100,000+ tutors teach 90+ languages to learners in 180 countries - and we’re only getting started. As a category-defining company, we’re shaping what the future of learning looks like at global scale.

Every Preply lesson sparks change, fuels ambition, and drives progress that matters. Joining Preply means helping define the future of education at global scale, and building something that truly matters for millions of people, every day.

About the role

As Preply scales its AI-powered learning platform, we’re looking for an experienced Senior ML Platform/Ops Engineer to help productionize machine learning systems with high reliability, performance, and observability. You’ll work at the intersection of ML, data engineering, and cloud infrastructure enabling fast, secure, and reproducible model development from training to deployment. We’ve reached 90%+ adoption of AI coding tools across engineering, and we’re now moving towards more autonomous, AI-Augmented development at scale. At Preply, engineers have direct access to the best tools available, with the freedom to use them fully and experiment as they build.

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

What you’ll be doing

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)

What we’re looking for

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