Lead ML/AI Platform Engineer
Tasks
- Build production ML systems
- Create design documents and executive recommendations
- Define ML AI engineering standards and tooling strategy
- Design ML/AI platform architecture
- Design scalable ML services and API contracts
- Develop agentic workflows and guardrails
- Drive model monitoring and drift detection
- Establish MLOps practices
- Implement GenAI LLM RAG evaluation and fine tuning
- Integrate ML services with Java microservices
- Maintain reproducibility and experiment tracking
- Manage model registry and feature pipelines
- Mentor engineers and guide architecture decisions
- Own model serving and inference pipelines
- Own training infrastructure
- Productionize models with data science
- Set ML roadmap and technical leadership
Perks/Benefits
- 401k matching
- Disability and life insurance
- Equity compensation
- 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
- Team building events
Skills/Tech-stack
AWS Glue | AWS Lambda | AWS Step Functions | Agentic Workflows | Amazon Athena | Amazon Bedrock | Amazon Redshift | Amazon S3 | Amazon SageMaker | Apache Spark | Drift Detection | Experiment tracking | Fine Tuning | Infrastructure as Code | Java | JupyterLab | Kafka | Kinesis | LightGBM | LoRA | MLOps | MLflow | Model Drift | Model Drift Detection | Model Evaluation | Model Monitoring | Model Registry | NumPy | PGVector | Pandas | Pinecone | Prompt engineering | PyTorch | Python | QLoRA | RAG | SQL | Scikit-learn | Step Functions | TensorFlow | Vector Search | XGBoost | “as-code”
Education
N/A
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