Lead ML/AI Platform Engineer
Tasks
- Build research to production pipelines and deployment processes
- Create technical design documents and executive recommendations
- Define ML AI engineering standards and tooling strategy
- Design model serving and inference pipelines
- Design scalable ML services and API contracts for Java microservices
- Design training infrastructure
- Develop RAG prompt engineering and evaluation strategies
- Drive LLM fine tuning and agentic workflow engineering
- Implement monitoring drift detection and reproducibility
- Manage model registries and feature pipelines
- Oversee model training tuning registration and deployment
- Own ML AI platform architecture and operations
- Provide technical leadership and mentorship
Perks/Benefits
- 401k matching
- Disability insurance
- Equity compensation package
- Flexible time off
- Half-day Fridays
- Learning and career development
- Life insurance
- Medical, dental, and vision coverage
- Paid time off for volunteering
- Phone and internet stipend
- Remote work setup reimbursement
- Team building events
Skills/Tech-stack
AWS Glue | AWS Lambda | AWS S3 | AWS SageMaker | AWS Step Functions | Amazon Athena | Amazon Bedrock | Amazon Redshift | Apache Spark | Drift Detection | Experiment tracking | Feature Pipelines | Inference Pipelines | Infrastructure as Code | Java | JupyterLab | Kafka | Kinesis | LightGBM | LoRA | MLOps | MLflow | Model Monitoring | Model Registry | NumPy | PGVector | Pandas | Pinecone | Prompt engineering | PyTorch | Python | Q LoRA | RAG | SQL | Scikit-learn | Step Functions | TensorFlow | Vector Databases | XGBoost | “as-code”
Education
N/A
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