Senior Machine Learning Operations Engineer
Tasks
- Build CI CD for ML including model registry and retraining triggers
- Build and operate ML production platform on AWS and Snowflake
- Create champion challenger shadow and canary deployment patterns
- Design and operate real time inference endpoints with latency budgets
- Implement feature store with online offline parity
- Integrate LLM APIs into production paths including RAG and evals
- Lead MLOps incident response and postmortems
- Optimize cost and performance with autoscaling caching and batching
- Own ML path from trained model to production endpoint
- Set up drift detection data quality and model monitoring with paging
- Stand up batch scoring pipelines with freshness and cost SLAs
- Use AI coding agents to accelerate infrastructure and model serving
Perks/Benefits
- 401k match
- Commuter savings
- Dental insurance
- Disability insurance
- Employee resource groups
- Flexible paid time off
- Health care FSA
- Life insurance
- Medical insurance
- Professional development reimbursement
- Skills training
- Vision insurance
Skills/Tech-stack
AWS SageMaker | Amazon Bedrock | Anthropic API | Batch Transform | CI/CD | Canary Release | Champion Challenger | DBT | DBT Core | Data Quality | Data quality monitoring | Distributed Systems | Docker | Drift Detection | ECS | Feast | Feature Store | Flink | IAM | Kafka | Kubernetes | Lambda | Langchain | Langfuse | Model Monitoring | Model Registry | Observability | Online Offline Parity | OpenAI API | PGVector | Pinecone | Python | Quality monitoring | RAG | S3 | SageMaker Batch Transform | SageMaker Endpoints | SageMaker Feature Store | SageMaker Model Registry | SageMaker Training | Sagemaker Pipelines | Secrets Manager | Shadow Deployment | Snowflake | Snowflake Cortex | Snowpark ML | Snowpipe Streaming | Tecton | Terraform | VPC | Vector Databases
Education
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