Principal AI Engineer
Tasks
- Add regression detection in CI
- Build agent memory infrastructure
- Build hybrid retrieval pipeline
- Build inference and intelligence layer
- Build structured extraction pipelines
- Capture expert judgment as first class data
- Create domain meaningful API abstractions
- Create retrieval feedback loops
- Create versioned graph snapshots
- Deploy real time inference endpoints
- Design domain knowledge graph
- Design evaluation with ground truth labels
- Develop MCP servers and interfaces
- Develop graph enhanced retrieval
- Develop structured retrieval queries
- Ensure determinism and audit trail
- Implement entity resolution pipelines
- Implement experiment tracking and model registry
- Implement observability for agent execution
- Implement retrieval planning and orchestration
- Implement system level evaluation frameworks
- Implement tool access and permissioning
- Integrate LLM APIs with cost management
- Support SDK development
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Anthropic API | Artificial Intelligence | Continuous integration | Databricks | Deployment Automation | Entity Extraction | Entity Resolution | Experiment tracking | Graph Databases | Inference | Knowledge graph | LLM integration | Language Models | Large Language Models | Logging | MLflow | Machine Learning | Model Registry | Model Serving | Monitoring | Node.js | Observability | OpenAI API | Prompt Caching | Python | Rate Limiting | Retrieval-Augmented Generation | Structured Query | Terraform | Tracing | Unity Catalog | Vector Databases | Vector Search
Education
N/A
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