F
MLOps Team Lead
On-siteNot specifiedDevOps & SREEnterprise SoftwareArtificial Intelligence
Lead a small MLOps engineering team developing the internal machine learning platform, infrastructure, and production pipelines used by multiple research and data science teams.
Responsibilities
- Lead, mentor, and grow 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 reviews while setting engineering standards.
- Drive model-lifecycle and infrastructure milestones across the team.
- Partner with R&D and stakeholders to translate research requirements into scalable systems.
- Evolve the internal ML platform, including migration from Dask to Ray.
- Maintain models and data pipelines supporting data science workflows across structured and unstructured data sources.
Requirements
- Bachelor’s or master’s degree in computer science, mathematics, engineering, or a related field.
- At least 5 years of commercial Python experience and 3 years of hands-on production MLOps experience.
- Experience leading or managing engineers with responsibility for people and delivery.
- Experience owning the ML lifecycle, including training, deployment, monitoring, and retraining.
- Experience with Dagster or Airflow, a major cloud provider, distributed computing, Docker, and Kubernetes.
- Commercial experience developing and maintaining scalable machine learning systems.
- Fluent written and spoken English.
Nice to have
- Experience with Dagster, Dask, Ray, or Spark.
- Experience implementing distributed algorithms in Python.
- Experience with forecasting, pricing optimization, or traditional predictive ML systems.
- Background in aviation, demand forecasting, or price optimization.
- Strong understanding of data structures and algorithms.