Machine Learning Engineer

This is a fully remote position, open to applicants in Spain.

📋 Description

• Design, develop, and maintain the MLOps platform, which includes experiment tracking, model registry, versioning, and reproducible training pipelines.

• Implement CI/CD methodologies for machine learning, featuring automated testing, validation gates, and promotion workflows from development to production.

• Establish standards and tools for feature stores, model artifacts, and reproducibility of environments.

• Transition models from research or prototype phases to robust, scalable production services.

• Develop low-latency, high-availability serving infrastructure for batch processing, online, and real-time inference.

• Set up monitoring for model performance, data drift, and concept drift, including alerting systems and rollback procedures.

• Collaborate with data science teams to optimize models for production requirements such as latency, cost, and scalability.

• Automate the processes for retraining, evaluation, and deployment pipelines.

• Create self-healing and auto-rollback systems.

• Develop tools that enable ML practitioners to deploy models without needing extensive infrastructure knowledge.

• Integrate ML models with Kafka, Kinesis, or Flink for real-time feature computation and inference.

• Design low-latency feature pipelines that connect batch and streaming data sources.

• Ensure consistency between offline training and online serving feature computations.

• Design and implement agentic workflows, including LLM-based agents and tool-calling pipelines, in conjunction with traditional ML models.

• Build observability, guardrails, and evaluation frameworks for dependable production agentic systems.

• Investigate the use of agents to automate aspects of the ML lifecycle, such as monitoring, triage, and retraining decisions.

• Collaborate closely with data science, platform, and product teams.

⛳️ Requirements

• 5 to 8 years of experience in ML engineering, MLOps, or backend infrastructure with machine learning systems in production.

• Solid software engineering principles; capable of managing services from start to finish.

• Familiarity with model serving frameworks (Seldon, KServe, BentoML, TorchServe, or similar) and orchestration tools (Airflow, Kubeflow, MLflow, or similar).

• Practical experience with streaming systems (Kafka, Kinesis, Flink, or similar).

• Knowledge of containerization and orchestration (Docker, Kubernetes).

• Experience with observability tools (metrics, tracing, logging) for machine learning or distributed systems.

• Excellent communication skills and comfortable collaborating across functions with data science, platform, and product teams.

• Proficient in English.

• Located in Europe.

🏝️ Benefits

• Competitive Compensation

• Remote Work: you can work from anywhere

• Home Office Bonus: a one-time allowance to set up your ideal home office

• Work Equipment

• Stock Options

• Health Plan wherever you are

• Flexible Days Off

• Language, Professional, and Personal Growth courses