Machine Learning Ops Engineer

hace 3 horas

Guadalajara, Jalisco, México ATG (Auction Technology Group) A tiempo completo

Auction Technology Group is expanding its team in Guadalajara, Mexico We are looking for exceptional engineering talent to join the team and help us transform the Auction industry.

What will you bring to the team?

We are making a significant investment in creating a user experience that meets the expectations of our customers. Not only do you put the customer at the heart of everything you do, but you are adept at enabling data-driven decisions to design and deliver strategic projects. You will be comfortable working cross-functionally with Product, Engineering, MLOps, and Analytics teams to develop our products and improve the end user experience. You should have a strong track record of successful prioritization, meeting critical deadlines and enthusiastically tackling challenges with an eye toward problem solving.

Key Responsibilities

  • Design, build, and maintain end-to-end MLOps pipelines supporting the complete model lifecycle, from development and training to deployment, monitoring, and retraining
  • Deploy and operationalize ML models for search ranking, recommendations, computer vision, and NLP in production, ensuring high availability and performance at scale
  • Implement containerization (Docker) and orchestration (Kubernetes) strategies for scalable model serving across multiple environments
  • Develop comprehensive monitoring systems to track model performance, data drift, and system health, with automated retraining pipelines triggered by performance degradation
  • Build CI/CD pipelines tailored for ML workflows, including automated testing, validation, and deployment
  • Establish model versioning, experiment tracking, and registry practices using MLflow or similar platforms
  • Create standardized, reusable ML workflows and self-service tools that enable efficient model development and deployment
  • Implement feature stores and data versioning systems to ensure reproducibility across experiments
  • Optimize model serving infrastructure to handle millions of daily requests with low latency
  • Implement A/B testing frameworks and experimentation platforms for rigorous model evaluation
  • Collaborate with ML Engineers and Software Engineers to integrate models into production systems built with Python, FastAPI, and Elasticsearch

Required Skills:

  • BSc or MSc in Computer Science, Data Science, Engineering, or equivalent practical experience
  • 3+ years building and maintaining ML pipelines and infrastructure in production environments
  • Strong Python programming with software engineering best practices (testing, code review, documentation)
  • Proven experience with MLOps platforms (MLflow, Kubeflow, Airflow, or similar)
  • Hands-on experience with Docker, Kubernetes, and CI/CD tools
  • Experience with cloud platforms (AWS, Azure, or GCP) and infrastructure-as-code (Terraform, CloudFormation)
  • Familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn) and model serving frameworks
  • Experience with data pipeline orchestration and workflow management tools
  • Knowledge of monitoring and observability solutions (Prometheus, Grafana, ELK stack)

  • Strong problem-solving abilities and collaborative mindset for cross-functional teamwork

  • Nice-to-Have:

  • Experience with feature stores (Feast, Tecton) and data versioning tools (DVC, Pachyderm)

  • Familiarity with Elasticsearch, vector databases, and embedding systems
  • Experience with GPU/TPU management and optimization
  • Background in eCommerce, marketplace, or auction platforms

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