Lead ML/AI Platform Engineer
Tasks
- Build production ML systems
- Create technical design documents and executive recommendations
- Define ML AI engineering standards
- Design model serving and inference pipelines
- Design scalable ML services and API contracts
- Develop RAG and LLM evaluation strategies
- Drive MLOps model monitoring and drift detection
- Execute prompt engineering and fine tuning
- Implement experiment tracking and reproducibility
- Integrate ML services with Java microservices
- Lead ML AI platform architecture
- Manage model registry and feature pipelines
- Mentor engineers and guide architecture decisions
- Operate training infrastructure
Perks/Benefits
- 401k matching
- Disability insurance
- Flexible time off
- Fully remote work environment
- Half-day Fridays
- Learning and career development
- Life insurance
- Medical, dental & vision coverage
- Paid time off for volunteering
- Phone and internet stipend
- Remote Work Reimbursement
- Team building events
- Volunteering time off
Skills/Tech-stack
AWS Glue | AWS Lambda | AWS SageMaker | AWS Step Functions | AgentCore) | Amazon Athena | Amazon Bedrock | Amazon Redshift | Amazon S3 | Apache Spark | Drift Detection | Experiment tracking | Hosted Inference | Inference Pipelines | Java | JupyterLab | LLM Evaluation | LightGBM | MLOps | MLflow | Model Drift | Model Drift Detection | Model Monitoring | Model Registry | NumPy | PGVector | Pandas | Pinecone | Prompt engineering | PyTorch | Python | RAG | SQL | Scikit-learn | Step Functions | TensorFlow | Vector Search | XGBoost
Education
N/A
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