Overview
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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, xqbhyrx 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 (Santiago de Compostela)
🏢 Fortra
📍 Santiago de Compostela