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MLOps Team Lead
PresencialTiempo completoAI EngineeringEnterprise SoftwareData Infrastructure
Lead a small team of MLOps engineers building and operating an internal machine learning platform for research workflows and production model pipelines. This hands-on role combines technical leadership, team development, infrastructure ownership, and software engineering.
Responsabilidades
- Lead, mentor, and develop a team of MLOps engineers while owning delivery and technical quality.
- Own infrastructure and pipeline initiatives from design through production.
- Contribute to system design and code, review implementation, and establish engineering standards.
- Drive milestones for machine learning model lifecycle and infrastructure ownership.
- Partner with research and other stakeholders to convert requirements into robust, scalable systems.
- Build and maintain models and data pipelines supporting data science workflows.
- Ensure the accuracy, consistency, and efficiency of training and inference data across structured and unstructured sources.
- Evolve the machine learning platform, including migration from Dask to Ray.
Requisitos
- Bachelor’s or master’s degree in computer science, mathematics, engineering, or a related field.
- At least 5 years of commercial Python experience.
- At least 3 years of hands-on commercial MLOps experience in production.
- Experience managing or leading engineering teams, including responsibility for people and delivery.
- Hands-on ownership of the machine learning model lifecycle, including training, deployment, monitoring, and retraining.
- Experience with pipeline orchestrators such as Dagster or Airflow.
- Experience with a major cloud provider such as GCP, AWS, or Azure.
- Experience with distributed computing systems, Docker, and Kubernetes.
- Commercial experience developing and maintaining scalable machine learning systems.
- Fluent written and spoken English.
Se valora
- Experience with Dagster, Dask, Ray, or Spark.
- Experience implementing distributed algorithms in Python.
- Background in predictive, forecasting, or pricing-optimization machine learning systems.
- Experience in aviation, demand forecasting, or price optimization.
- Strong understanding of data structures and algorithms.