MLOps Engineer
Tasks
- Automate testing and operational standards
- Build and maintain data pipelines
- Build versioned model deployment workflows
- Collaborate with AI engineers to make models production ready
- Define failure modes and recovery strategies
- Design scalable inference pipelines
- Design, build, and operate ML platform
- Implement ML CI CD pipelines
- Implement monitoring for drift and system health
- Implement observability for model performance and data quality
- Implement rollout rollback and monitoring
- Improve environment parity
- Ingest validate and transform IoT sensor data
- Optimize latency throughput and cost
Perks/Benefits
- Autonomy and ownership
- Brainfood
- Career development opportunities
- Flat hierarchy
- Unlimited coffee
- Unlimited snacks
Skills/Tech-stack
CI/CD | Cloud infrastructure | Data Validation | GCP | Infrastructure automation | Latency optimization | Machine Learning | Model Deployment | Model Drift | Model Monitoring | Observability | Performance Tuning | Python | Throughput Optimization | Time Series | TypeScript | Version control
Education
N/A
Roles
Regions
Countries
States
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