Machine Learning Engineer

Enfuce; Madrid; Presencial; EUR 70.000 - 110.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Healthcare and insurance

Flexible time off

Team activity budget

Learning budget

Descripción de la vacante

Enfuce is seeking a Machine Learning Engineer to build and maintain scalable ML infrastructure and platforms. You will collaborate with data scientists and engineers to deploy, monitor, and govern AI systems in production.

You will drive reproducible workflows, experiment tracking, and automated pipelines using modern MLOps tools across cloud environments, including support for LLM and Generative AI applications.

Formación

• End-to-end ML lifecycle expertise, from data to governance.
• Experience with Docker, container orchestration, and CI/CD for ML.
• Cloud platform proficiency (AWS/Azure/GCP) with production monitoring.
• GIT workflows and Infrastructure as Code (Terraform/CloudFormation).
• Experience deploying LLM or Generative AI applications is a plus.

Responsabilidades

• Build and maintain ML infrastructure and tooling for scalable AI solutions.
• Own production lifecycle of ML systems: data pipelines, deployment, monitoring, and CD.
• Implement reproducible workflows and automated ML pipelines.
• Collaborate with Data Scientists and Engineers to productionise AI models.
• Evaluate new MLOps tools and best practices, including LLMs.

Conocimientos

ML Lifecycle

Docker/Kubernetes

Cloud Platforms

Git & IaC

LLM/Generative AI

Python & SQL

Educación

Bachelor’s or Master’s in CS/ML/SE

Herramientas

MLflow

Snowflake

dbt

Snowpark ML

Vertex AI

Amazon SageMaker

Descripción del empleo

Responsibilities

• 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, and scale machine learning solutions.
• Evaluate and implement new MLOps tools, frameworks, and best practices, including support for LLM and agentic AI applications.

Benefits

• Extended 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.
• Flexible paid time off: We offer a flexible 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.
• Regular 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.
• Individual learning budget: You get a yearly learning budget to use for courses and other relevant learning opportunities that help you develop your skills.

Qualifications

• 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 deploying LLM or Generative AI applications is a strong advantage, along with excellent problem‑solving, communication, and collaboration skills.
• Experience with MLflow for experiment tracking, model registry, versioning, and model lifecycle management.
• Familiarity with feature stores, model registries, artifact repositories, and modern MLOps practices.
• Experience with modern ML platforms such as Snowflake, dbt, Snowpark ML, Vertex AI, or Amazon SageMaker.
• Strong Python programming skills and proficiency with SQL.