Responsibilities
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- 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.
- Implement experiment tracking, model versioning, model registries, and artifact management using MLOps best practices.
- 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.
- Manage the end‑to‑end model lifecycle, including retraining, rollback, reproducibility, governance, and auditability.
- 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,
📌 Machine Learning Engineer (Madrid)
🏢 Enfuce
📍 Madrid