Related skills
docker python kubernetes ci/cd observability📋 Description
- Architect deployment pipelines moving models from research to production with confidence
- Build AB testing infrastructure for controlled rollouts and impact
- Implement monitoring for model performance: accuracy, latency, drift, alerts
- Develop automated retraining pipelines triggered by data changes or performance degradation
- Create build and test environments that mirror production for feedback
- Establish model versioning and rollback capabilities for safe deployments
🎯 Requirements
- 4+ years in MLOps, DevOps, or ML infrastructure
- Proficiency in Python and ML automation tooling
- CI/CD systems and pipelines for software and model delivery
- Docker and Kubernetes for containerized workloads
- Deploying and serving ML models in production
- Knowledge of model evaluation, validation, and QA
- Monitoring and observability principles for ML systems
- Strong problem-solving with a bias toward automation
🎁 Benefits
- Medical, dental, vision benefits
- Annual wellness stipend
- Mental health support
- 401(k) plan with company match
- Unlimited PTO
- Generous paid parental leave
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