Applied Machine Learning Engineer - ML & AI Systems
Tasks
- Analyze production model behavior
- Build predictive models
- Collaborate cross functionally on ML requirements
- Conduct experimentation feature engineering hyperparameter tuning
- Define model performance metrics
- Deploy production ML pipelines
- Develop batch and real time inference
- Document models pipelines and systems
- Ensure model reproducibility versioning governance
- Evaluate generative AI for enterprise use cases
- Implement CI/CD workflows for ML lifecycle
- Implement distributed training
- Implement model monitoring logging drift detection
- Implement retrieval-augmented generation pipelines
- Integrate models with enterprise applications
- Optimize business decisions
- Promote engineering best practices
- Serve models via APIs
Perks/Benefits
Skills/Tech-stack
APIs | BigQuery | CI/CD | Cloud Run | Cloud platform | Data Drift | Data Drift Detection | Dataflow | Deep learning | Drift Detection | Feature Engineering | Google Cloud | Google Cloud Platform | Hyperparameter Tuning | Kubernetes | Language Models | Large Language Models | Logging | Machine Learning | Model Governance | Model Monitoring | Optimization Algorithms | Predictive Modeling | Pub/Sub | PyTorch | Python | RAG | Retrieval-Augmented Generation | Scikit-learn | TensorFlow | Versioning | Vertex AI
Education
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