ph3Responsibilities /h3 ul liAs 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. /li liWorking 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. /li liYou 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. /li liThis role involves working with cloud‑native technologies, modern MLOps platforms, and production‑grade AI systems in the financial services domain. /li liDesign, build, and maintain scalable MLOps infrastructure for machine learning and Generative AI applications. /li liDevelop automated training, validation, testing, deployment, and CI/CD pipelines for machine learning models. /li liImplement experiment tracking, model versioning, model registries, and artifact management using MLOps best practices. /li liBuild and maintain workflow orchestration, feature engineering, and data processing pipelines. /li liMonitor production ML systems, including model performance, data quality, drift detection, latency, and overall system health. /li liManage the end‑to‑end model lifecycle, including retraining, rollback, reproducibility, governance, and auditability.



/li liContainerize ML workloads with Docker and deploy scalable services using cloud‑native technologies and orchestration platforms. /li liDevelop and maintain Infrastructure as Code (IaC) for AI platforms and cloud resources. /li liCollaborate with Data Scientists and software engineers to productionise, optimise, and scale machine learning solutions. /li liEvaluate and implement new MLOps tools, frameworks, and best practices, including support for LLM and agentic AI applications. /li /ul h3Benefits /h3 ul liExtended healthcare and insurance: We offer occupational healthcare and well‑being programmes in all locations, including mental well‑being coaching. The specific programmes might vary depending on your location. /li liFlexible paid time off: We offer a adaptable paid time off policy, providing up to 5 weeks of annual vacation days and paid family leave (subject to country regulations). Additionally, you can benefit from hybrid or remote work options, promoting a healthy work‑life balance. /li liRegular fun with your team: To spend other than work‑related time with your teammates, you get a team activity budget for three quarters a year.



The fourth quarter is reserved for a company‑wide event. /li liIndividual learning budget: You get a yearly learning budget to use for courses and other relevant learning opportunities that help you develop your skills. /li /ul h3Qualifications /h3 ul liStrong understanding of the end‑to‑end machine learning lifecycle, including experimentation, deployment, monitoring, retraining, and governance. /li liExperience with Docker, containerised ML workloads, and container orchestration platforms such as Kubernetes. /li liBachelor’s or Master’s degree in Computer Science, Machine Learning, Software Engineering, or a related field. /li liHands‑on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, including production monitoring and observability. /li liExperience with Git, software engineering best practices, and Infrastructure as Code (e.g., Terraform or CloudFormation). /li liExperience deploying LLM or Generative AI applications is a strong advantage, along with excellent problem‑solving, communication, and collaboration skills. /li liExperience with MLflow for experiment tracking, model registry, versioning, and model lifecycle management. /li liFamiliarity with feature stores, model registries, artifact repositories, and modern MLOps practices. /li liExperience with modern ML platforms such as Snowflake, dbt, Snowpark ML, Vertex AI, or Amazon SageMaker. /li liStrong Python programming skills and proficiency with SQL. /li /ul /p #J-18808-Ljbffr

📌 Machine Learning Engineer (Madrid)
🏢 Enfuce
📍 Madrid