Senior MLOps & AI Infrastructure Engineer
San Jose, California, United States, United States
USD 149K-215K Senior-level Full Time
Tasks
- Apply transfer learning quantization pruning distillation RLHF
- Automate infrastructure provisioning with Terraform
- Benchmark and optimize model inference performance on GPU TPU clusters
- Build MLOps infrastructure experiment tracking model registry feature stores automated retraining
- Build data pipelines for structured and unstructured datasets
- Build monitoring alerting observability for drift and data quality
- Containerize ML workloads using Docker
- Design ML pipelines for training evaluation deployment
- Develop fine tune and deploy LLMs GNNs reinforcement learning agents
- Implement A B testing frameworks and shadow deployments
- Implement CI CD continuous training for ML models
- Implement data versioning and lineage tracking
- Integrate HPC schedulers for distributed training
- Manage cloud ML infrastructure on AWS SageMaker Azure ML GCP Vertex AI
- Mentor junior engineers and define ML best practices
- Orchestrate ML workloads with Kubernetes
- Support HPC distributed training jobs
Perks/Benefits
- N/A
Skills/Tech-stack
AWS SageMaker | Airflow | Arize | Azure Machine Learning | Bash | CI/CD | Cluster management | DVC | Data Versioning | Data parallelism | Deep learning | DeepSpeed | Delta Lake | Docker | ELK Stack | Evidently AI | Experiment tracking | FSDP | Feast | Feature Store | GPU Cluster | GPU Cluster Management | GPU Computing | Go | Google Vertex | Google Vertex AI | Grafana | Hugging Face | JAX | Kubeflow | Kubernetes | LSF | Language Models | Large Language Models | Linux | MLOps | MLflow | Machine Learning | Model Parallelism | Model Registry | Prometheus | PyTorch | Python | RAG | SQL | Scikit-learn | Slurm | TensorFlow | Terraform | Vertex AI | Weights and Biases | XGBoost
Education
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