Lead ML/AI Platform Engineer
Tasks
- Build inference pipelines and production integrations
- Create technical roadmap for AI ML opportunities
- Define ML AI architecture standards and tooling strategy
- Design scalable ML services and API contracts for microservices
- Design training infrastructure and model serving systems
- Develop GenAI and LLM engineering strategies
- Drive MLOps for monitoring drift detection and experiment tracking
- Enable research to production model deployment pipelines
- Implement RAG evaluation prompt engineering and fine tuning
- Mentor engineers and guide complex architecture decisions
- Own ML AI platform architecture and operations
- Produce design documents and executive level recommendations
Perks/Benefits
- Disability insurance
- Flexible time off
- Half-day Fridays
- Learning and career development
- Life insurance
- Medical/Dental/Vision insurance
- Paid time off for volunteering
- Phone and internet stipend
- Remote work setup reimbursement
Skills/Tech-stack
API Gateway | AWS API | AWS API Gateway | AWS Glue | AWS Lambda | AWS Step Functions | Amazon Athena | Amazon Bedrock | Amazon Redshift | Amazon S3 | Amazon SageMaker | Apache Spark | Cost observability | Deployment Pipelines | Drift Detection | Evaluation | Experiment tracking | Inference Pipelines | Java | JupyterLab | LightGBM | LoRA | MLOps | MLflow | Model Drift | Model Drift Detection | Model Monitoring | Model Registry | NumPy | PGVector | Pandas | Pinecone | Prompt engineering | PyTorch | Python | QLoRA | RAG | Scikit-learn | Step Functions | TensorFlow | Vector Search | XGBoost
Education
N/A
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