Senior MLOps & AI Infrastructure Engineer
San Jose, California, United States, United States
USD 149K-215K Senior-level Full Time
Tasks
- Apply model optimization techniques transfer learning quantization pruning distillation RLHF
- Automate infrastructure provisioning with Terraform or CloudFormation
- Benchmark and optimize model inference on GPU TPU clusters
- Build MLOps infrastructure for experiment tracking model registry feature stores retraining
- Build data pipelines for structured and unstructured datasets
- Build monitoring alerting and observability for model drift data quality system health
- Containerize ML workloads with Docker and deploy on Kubernetes
- Design ML pipelines for training evaluation deployment
- Develop fine tune and deploy large scale models including LLMs and GNNs
- Implement A B testing and shadow deployments
- Implement CI/CD pipelines for ML models
- Implement data versioning and lineage tracking
- Implement feature engineering systems and data quality checks
- Manage cloud ML infrastructure on AWS SageMaker Azure ML and GCP Vertex AI
- Productionize research models and mentor engineers
- Support HPC schedulers for distributed training jobs
Perks/Benefits
- N/A
Skills/Tech-stack
AWS SageMaker | Airflow | Arize | Azure Machine Learning | Bash | CI/CD | CloudFormation | DVC | Data Lineage | Data Quality | Data Versioning | Data quality monitoring | Docker | ELK Stack | Evidently AI | Experiment tracking | Feast | Feature Store | Go | Google Vertex | Google Vertex AI | Grafana | Hugging Face | JAX | Kubeflow | Kubernetes | LSF | MLflow | Machine Learning | Model Registry | Prometheus | PyTorch | Python | Quality monitoring | RAG | Retrieval-Augmented Generation | SQL | Scikit-learn | Slurm | TensorFlow | Terraform | Vertex AI | Weights and Biases | XGBoost
Education
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