Sr Machine Learning Engineer
Tasks
- Add explainability and bias monitoring
- Build feature stores and model registries
- Build full-stack AI applications
- Co own model performance KPIs
- Detect model drift and bias
- Develop experiment tracking libraries
- Engineer end-to-end ML pipelines
- Expose secure REST and gRPC APIs
- Harden research code into production micro-services
- Implement data encryption and access policies
- Implement quantization and pruning
- Optimize model performance and cost at scale
- Package models into Docker and Kubernetes
- Perform exploratory data analysis
- Prototype and benchmark new algorithms
- Provide observability with metrics and tracing
- Track data lineage
- Tune GPU and CPU auto scaling policies
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | AWS Bedrock | Access Control | Agent Frameworks | Agile | Amazon SageMaker | Amazon SageMaker Pipelines | Azure | Bedrock | Bias detection | Clustering | Convolutional Neural Networks | Data Lineage | Data encryption | Deep learning | Dimensionality Reduction | Distributed tracing | Docker | Drift Detection | Experiment tracking | Explainability | Feature Store | GRPC | GitHub Actions | Google Cloud | Java | Kubeflow | Kubernetes | Language Models | Large Language Models | MLOps | Machine Learning | Model Registry | Neural Networks | Prompt engineering | Pruning | Python | Quantization | RAG | REST | Recurrent Neural Networks | Regression | SAFe | Sagemaker Pipelines | Time Series | Transformers | Vector Databases
States
Cities
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