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)

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

A generous monthly allowance for lessons on Preply.com
Learning & Development budget and time off for your self-development
A competitive financial package with equity, leave allowance and health insurance
Access to free mental health support platforms
The opportunity to unlock the potential of learners and tutors through language learning and teaching in 175 countries (and counting!)

Senior Machine Learning Platform/Ops Engineer

Key skills