Senior Machine Learning Operations Engineer

Senior Machine Learning Operations Engineer

Location: Remote from Spain (Spanish employment contract

Join a transformative data and AI platform initiative aimed at modernizing enterprise-scale capabilities and enabling real-time decision-making. This project delivers a comprehensive roadmap covering AI, MLOps, data governance, and platform scalability, supporting a shift towards data-first operations and intelligent automation.

Requirements:

• 4+ years of experience in DevOps or MLOps roles with a strong focus on ML model lifecycle automation and infrastructure scalability.
• Proficiency with AWS SageMaker and associated AWS services such as S3, IAM, Secrets Manager, and CloudWatch.
• Practical experience with ML pipeline tooling such as MLFlow for model tracking and Apache Airflow for workflow orchestration.
• Solid skills in scripting and automation using Python, Bash, and YAML.
• Experience building and maintaining CI/CD workflows.
• Familiarity with Infrastructure as Code principles and hands-on experience with Terraform.
• Comfortable working in cross-functional teams with Data Scientists, helping bridge experimentation and production environments.

Will be a plus:

• Experience with data lake formats for managing large-scale tabular data in ML workflows.
• Knowledge of advanced resource optimization and auto-scaling strategies.
• Familiarity with FastAPI for building RESTful ML service interfaces.
• Prior work in highly regulated domains such as identity verification, where traceability, explainability, and compliance is critical.
• Understanding of GenAI/LLM workflows and how to enable scalable infrastructure to support those pipelines.

Responsibilities:

• Design and implement Machine Learning infrastructure within AWS Sagemaker for scalable, secure, and automated model training, deployment, and monitoring pipelines.
• Set up and maintain MLFlow for model tracking, experiment management, and governance with a focus on production-readiness and reproducibility.
• Integrate data sources and pipeline orchestration using Apache Airflow to feed data into SageMaker model training workflows.
• Build CI/CD pipelines for robust testing, linting, vulnerability scanning, and seamless model deployment.
• Manage containerized workloads and services on AWS EKS.
• Automate and manage infrastructure provisioning using Terraform, ensuring repeatable and compliant infrastructure-as-code practices.
• Implement monitoring and observability, enabling fine-grained insights into pipeline performance, resource usage, and anomaly detection.
• Collaborate closely with Data Scientists to streamline experimentation workflows, optimize pipeline runtimes, and scale computational resources.