Machine Learning Systems Operations Engineer

Job Category

QuinStreet is looking for a skilled and motivated Machine Learning Platform Engineer to join our growing ML team in Mexico. In this role, you will be responsible for building, deploying, and maintaining robust and scalable machine learning infrastructure and pipelines. You will work closely with data scientists, data engineers, and software developers to ensure models are production-ready, secure, and deliver real business value.

This role will be based in Mexico.

 

Responsibilities

• Design and maintain scalable infrastructure for model training, serving, monitoring, and feature management.
• Build and manage data pipelines, feature stores, and metadata stores to support ML workflows.
• Optimize memory and compute efficiency for large-scale training and inference.
• Enable distributed training and deployment across heterogeneous hardware (CPU, GPU, etc.).
• Automate end-to-end ML workflows using orchestration tools like Airflow or Argo.
• Ensure high availability, observability, and reliability of ML systems in production.
• Collaborate with ML engineers, data scientists, and infrastructure teams to streamline development and deployment.
• Enforce security, compliance, and infrastructure best practices throughout the ML stack.
• Support the ML release process.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• 3+ years of experience in backend, infrastructure, or machine learning systems engineering
• Proficiency in Python and at least one backend language (Java, Go, or Scala)
• Hands-on experience with data storage technologies, including SQL and NoSQL databases, feature stores, and distributed data processing frameworks (e.g., Spark, Kafka)
• Experience with cloud platforms (e.g., AWS, GCP, or Azure), containerization using Docker, orchestration with Kubernetes, and CI/CD pipelines for ML workflows
• Familiarity with MLOps tools such as MLflow or Kubeflow, and distributed training frameworks (e.g., PyTorch)
• Strong foundation in Unix/Linux systems and scripting (e.g., Bash, Shell)

Nice to Have

• Experience scaling ML infrastructure in production environments
• Knowledge of model optimization techniques and hardware-aware deployment strategies
• Deep experience with MLOps tools (MLflow, Kubeflow)
• Advanced understanding of data stores and feature stores in large-scale ML systems
• Strong expertise in Python and Unix/Linux environments beyond basic proficiency

#LI-REMOTE