5 months ago
Barcelona, SpainSenior
Responsibilities
- Build and maintain ML pipelines for training, evaluation, and deployment using Databricks, MLflow, Airflow, DBT, SageMaker, and Tecton.
- Create reproducible, containerized model training environments and manage compute at scale, including spot and GPU autoscaling.
- Define and implement observability and alerting for model drift, data quality, feature coverage, and other ML system metrics.
- Design and scale batch and streaming data ingestion and feature transformation flows using Spark, BigQuery, and Kafka or equivalent technologies.
- Develop internal Python libraries and platform tooling to accelerate experimentation and model deployment.
- Ensure ML services are modular, testable, and monitored from the beginning.
- Explore and productionize LLM-based features, including retrieval pipelines, prompt evaluation, and model serving.
- Collaborate with ML Scientists, Backend Engineers, and Data Engineers and mentor peers on reliability, testing, and delivery.
Requirements
- 5+ years of experience designing and deploying machine learning systems in production.
- Proficiency in Python, SQL, and orchestration tools such as Airflow, Kubeflow, or Dagster.
- Experience with modern cloud platforms, preferably GCP or AWS, as well as Kubernetes and CI/CD workflows.
- Understanding of the ML model lifecycle, including training, validation, deployment, and monitoring.
- Hands-on DevOps experience with Git, Terraform-based infrastructure as code, logging and observability, and containerization with Docker and Kubernetes.
- Ability to work independently with ML Scientists and mentor peers in reliability, testing, and delivery.
- Exposure to LLM serving, vector databases, or GenAI-powered product flows.
- Deep hands-on expertise with AI tools, particularly agentic AI software development lifecycles.
Benefits
- Open, collaborative, dynamic, and diverse company culture.
- Monthly allowance for lessons on Preply.com.
- Learning and Development budget and time off for self-development.
- Competitive financial package including equity, leave allowance, and health insurance.
- Access to free mental health support platforms.
- Opportunity to support learners and tutors through language learning and teaching in countries worldwide.
Tech Stack
Apache AirflowApache KafkaApache SparkAWSDatabricksdbtDockerGitGoogle BigQueryGoogle Cloud PlatformKubernetesMLflowPythonSQLTerraform
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