Sr Machine Learning Engineer
Tasks
- Analyze user behavior analytics
- Apply quantization and pruning
- Build AI applications with workflow orchestration
- Build feature stores and model registries
- Collaborate on model performance KPIs
- Detect model drift and bias
- Engineer end-to-end ML pipelines
- Expose secure REST APIs
- Expose secure gRPC APIs
- Harden research code into production services
- Implement observability metrics and tracing
- Implement security and responsible-AI controls
- Integrate model services with user interfaces
- Maintain experiment tracking libraries
- Optimize model performance and cost
- Package models into Docker and Kubernetes
- Perform exploratory data analysis
- Prototype and benchmark new algorithms
- Select and tune machine learning algorithms
- Track data lineage and access policies
- Tune GPU and CPU auto scaling policies
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Access Control | Azure | Bias detection | CNN | Cloud platform | Clustering | Data Lineage | Data encryption | Deep learning | Dimensionality Reduction | Distributed tracing | Docker | Explainability | Feature Engineering | GRPC | Generative AI | GitHub Actions | Google Cloud | Google Cloud Platform | Hyperparameter Optimization | Java | Kubeflow | Kubernetes | Langchain | MLOps | Machine Learning | Model Deployment | Model Monitoring | Prompt engineering | Python | RAG | REST API | RNN | Regression | Sagemaker Pipelines | Semantic Kernel | Series forecasting | Time Series | Time Series Forecasting | Transformer Models | Vector Databases
Education
N/A
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