Senior Machine Learning Operations Engineer
Tasks
- Build and operate ML production platform
- Build model registry and CI CD for ML
- Collaborate with data engineering on standards and lineage
- Design and operate batch scoring pipelines
- Design and operate real time inference paths
- Implement champion challenger shadow and canary deployments
- Implement feature store with online offline parity
- Integrate LLM APIs into production pipelines
- Manage MLOps incident response and postmortems
- Own model deployment from trained model to production endpoint
- Right size endpoints and optimize cost performance
- Set up drift and quality monitoring with alerting
- Set up observability and paging for ML systems
Perks/Benefits
- 401k company match
- Dental insurance
- Disability insurance
- Employee resource groups
- Flexible paid time off
- Life insurance
- Medical insurance
- Ongoing skills training
- Pre-tax spending accounts
- Professional development reimbursement
- Vision insurance
Skills/Tech-stack
API Gateway | AWS | AWS Lambda | Amazon S3 | Amazon SageMaker | Autoscaling | Bedrock | CI/CD | CI/CD for ML | Canary Releases | Champion Challenger | DBT | DBT Core | Data Quality | Data quality monitoring | Distributed Systems | Docker | Drift monitoring | ECS | Feature Store | IAM | Kubernetes | Machine Learning | Machine Learning Pipelines | Model Monitor | Model Registry | Monitoring | Observability | OpenAI | Python | Quality monitoring | RAG | Request batching | SageMaker Model Monitor | Secrets Manager | Shadow Deployment | Snowflake | Snowflake Cortex | Snowflake RBAC | Snowpark ML | Terraform | VPC
Education
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