Responsibilities
Collaborate with engineering, product, and data science teams to identify business challenges suited to machine learning and AI solutions.
Develop tools and automate manual processes to improve operational efficiency, experimentation velocity, and reliability.
Build, integrate, and monitor end-to-end lifecycles for large-scale distributed machine learning systems.
Investigate model performance and identify data-quality and system-performance issues.
Enhance the forecasting ML pipeline, including weekly automated model retraining and deployment across production models.
Improve team capabilities in MLOps, deployment automation, and production machine learning systems.
Drive projects from concept through production and maintain and continuously improve their reliability.
Requirements
At least 5 years of experience building and maintaining production machine learning systems.
Deep expertise in MLOps, deployment automation, and model-serving infrastructure.
Strong software engineering skills with proficiency in Python, Docker, Kubernetes,



GitHub Actions, ArgoCD, Terraform, and Helm.
Experience with model-training orchestration, automated retraining pipelines, and A/B testing or variant management using Dagster, Airflow, or similar tools.
Experience designing scalable microservices, APIs, and complex service dependencies.
Ability to collaborate with data scientists to productionize research, backend teams on API integration, and product teams on customer needs.
Experience writing tested, maintainable, and well-documented code.
Preferred experience in healthcare or other regulated industries, forecasting or time-series algorithms, computer vision, DAG frameworks, or Flink.
Benefits
~ Competitive salary and stock options.
~ Versátil vacation policy.
~ Remote-first work environment with virtual and in-person team events.
~ Comprehensive health, dental, and vision insurance.
~16 weeks of parental leave for all parents.

📌 Senior Machine Learning Engineer (España)
🏢 Apella
📍 España