As a Machine Learning Engineer at Enfuce, you will build and maintain the infrastructure, tooling, and platforms that enable machine learning and generative AI solutions to be developed, deployed, and operated reliably at scale.
Working closely with Data Scientists and Data Engineers, you will own the production lifecycle of ML systems, from data pipelines and experiment tracking to model deployment, monitoring, and continuous delivery.
You will help establish MLOps best practices across the organization by building reproducible machine learning workflows, scalable infrastructure, and automation that accelerates the delivery of AI‑powered products.
This role involves working with cloud‑native technologies, modern MLOps platforms, and production‑grade AI systems in the financial services domain.
Design, build, and maintain scalable MLOps infrastructure for machine learning and Generative AI applications.
Develop automated training, validation, testing, deployment, and CI/CD pipelines for machine learning models.
Build and maintain workflow orchestration, feature engineering, and data processing pipelines.
Monitor production ML systems, including model performance, data quality, drift detection, latency, and overall system health.
Containerize ML workloads with Docker and deploy scalable services using cloud‑native technologies and orchestration platforms.
Develop and maintain Infrastructure as Code (IaC)



for AI platforms and cloud resources.
Collaborate with Data Scientists and software engineers to productionise, optimise, and scale machine learning solutions.
Extended healthcare and insurance: Additionally, you can benefit from hybrid or remote work options, promoting a healthy work‑life balance.
To spend other than work‑related time with your teammates, you get a team activity budget for three quarters a year. Individual learning budget: You get a yearly learning budget to use for courses and other relevant learning opportunities that help you develop your skills.
Strong understanding of the end‑to‑end machine learning lifecycle, including experimentation, deployment, monitoring, retraining, and governance.
Experience with Docker, containerised ML workloads, and container orchestration platforms such as Kubernetes.
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Software Engineering, or a related field.
Hands‑on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, including production monitoring and observability.
Experience with Git, software engineering best practices, and Infrastructure as Code (e.g., Terraform or CloudFormation).
Experience with modern ML platforms such as Snowflake, dbt, Snowpark ML, Vertex AI, or Amazon SageMaker.
Strong Python programming skills and proficiency with SQL.
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📌 Machine Learning Engineer - Fully Remote (Madrid)
🏢 Enfuce
📍 Madrid