Overview

In this role you enable data-science models to run reliably in production by owning the ML infra and operations on AWS and Kubernetes. You will design, deploy, monitor, and scale production ML services, collaborating with Data Scientists who retain model development. You’ll build CI/CD pipelines, IaC workflows, and robust APIs while enforcing security, observability, and cost-efficient operation. This position supports both existing products and new development, offering impact at scale and opportunities to mentor teammates.

Compensaciones / Ventajas

- competitive benefits and salaries
- personal and professional development opportunities
- flexibility
- growth opportunities
- collaborative culture

Responsabilidades

- Lead complex MLOps projects with autonomy and judgment
- Design, develop, test, and debug software for customer-facing and internal apps
- Operate production ML services including deployment, scaling, and monitoring
- Maintain Kubernetes deployments and AWS infrastructure for model-serving workloads
- Develop and maintain CI/CD pipelines and infrastructure-as-code for model-serving systems
- Create and maintain APIs, orchestration layers, and data interfaces for production models
- Collaborate with Data Scientists to productionize models and resolve integration issues
- Benchmark and stress-test ML/LLM services; ensure reliability and performance
- Establish logging, metrics, alerting, dashboards, incident response, and cost-effective operation




- Operate MLflow and Kubeflow for lifecycle, pipelines, and workflows
- Evaluate new technologies to strengthen systems and enforce engineering standards
- Mentor junior engineers and contribute to roadmaps and cross-team initiatives
- Share MLOps expertise and stay current with industry developments
- Develop domain expertise in at least one cybersecurity application area
- Document technical approaches and decisions

Requisitos principales

- 7+ years in engineering or related roles with hands-on production operations
- Strong cloud and software engineering foundations; MLOps experience
- Production architecture and API design experience in Python; Java or C++ a plus
- Comprehensive MLOps background: containerized model serving, CI/CD, IaC, observability, release management
- Hands-on AWS and Kubernetes operations including Docker, IAM, networking, monitoring, and incident troubleshooting
- CI/CD ownership; Jenkins and ArgoCD are strong pluses
- Experience deploying ML services in production and performing benchmarking/load testing
- MLflow & Kubeflow experience (learning ability okay if not yet proficient)
- Knowledge of PyTorch, TensorFlow, scikit-learn for integrating model work
- Cross-functional collaboration and project leadership experience
- Mentorship and strong communication skills
- Problem-solving and risk management abilities
- Cybersecurity domain interest

- collaboration
- clear communication
- mentorship
- MLOps
- Kubernetes
- AWS

📌 Sr. Machine Learning Engineer (España)
🏢 Fortra
📍 España