Lead AI/ML Platform Engineer
Tasks
- Build MLOps and LLMOps platform capabilities
- Create GPU accelerated compute environments
- Deploy and monitor model training pipelines
- Design cloud-native AI infrastructure
- Design secure multi tenant environments
- Develop model serving and inference scaling approaches
- Ensure observability and operational resiliency
- Implement model versioning and rollback
- Implement prompt management and latency optimization
- Lead engineering standards and design reviews
- Mentor engineers and provide technical direction
- Standardize vector database and orchestration patterns
Perks/Benefits
- 401k match
- Health care and wellness plans
- Paid Holidays
- Paid time off
- Referral services
- Tax advantaged accounts
- Tuition reimbursement
- Vehicle purchase discount
Skills/Tech-stack
AWS | AWS CDK | Airflow | CI/CD | CloudFormation | CloudWatch | Datadog | Docker | GPU Computing | Git | GitHub | GitHub Actions | Inference | Infrastructure as Code | Jenkins | Kubernetes | LLMOps | Langchain | MLOps | MLflow | Microservices | Model Deployment | Model Registry | Model Training | Prometheus | Prompt engineering | RAG Pipelines | Ray | Retrieval-Augmented Generation | Serverless computing | Terraform | Token optimization | Vector Databases | “as-code”
Education
N/A
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