Lead ML/AI Platform Engineer
Tasks
- Build model serving and inference pipelines
- Create feature pipelines and production integrations
- Define ML AI architecture standards and tooling strategy
- Design scalable ML services and API contracts with Java microservices
- Design training infrastructure
- Develop GenAI LLM strategies for RAG evaluation and fine tuning
- Drive MLOps practices for experiment tracking and model registry
- Implement monitoring drift detection and reproducibility
- Mentor engineers and guide complex architectural decisions
- Oversee model training tuning registration and deployment
- Own ML AI platform architecture and operations
- Produce design documents and executive recommendations
Perks/Benefits
- 401k matching
- Disability and life insurance
- Equity compensation package
- Flexible time off
- Fully remote work
- Half-day Fridays
- Learning and career development
- Medical, dental, and vision coverage
- Paid time off for volunteering
- Phone and internet stipend
- Remote work setup reimbursement
Skills/Tech-stack
API Design | AWS Glue | AWS Lambda | AWS Step Functions | AgentCore) | Amazon Athena | Amazon Bedrock | Amazon Redshift | Amazon S3 | Amazon SageMaker | Apache Spark | Cost observability | Deployment Patterns | Drift Detection | Experiment tracking | Feature Pipelines | Fine Tuning | Inference Pipelines | Java | JupyterLab | LightGBM | MLOps | MLflow | Model Evaluation | Model Monitoring | Model Registry | NumPy | PGVector | Pandas | Pinecone | Prompt engineering | PyTorch | Python | RAG | Reproducibility | Retrieval | SQL | Scikit-learn | Step Functions | TensorFlow | Vector Databases | XGBoost
Education
N/A
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