AI/LLM Application Engineer
Tasks
- Apply access control
- Build prompt pipelines
- Build retrieval pipelines
- Build test harnesses for OQ PQ
- Configure model routing
- Create output templates for QMS workflows
- Create representative datasets
- Create reviewer facing outputs
- Define metadata schemas
- Detect traceability gaps
- Develop AI LLM application services
- Generate draft evidence
- Generate evidence for quality workflows
- Implement AI agents for requirement quality checks
- Implement RAG services
- Implement audit logging
- Implement prompt regression tests
- Implement source citation mechanisms
- Improve observability across model calls
- Integrate enterprise systems read only
- Isolate projects
- Maintain prompt libraries
- Maintain technical documentation
- Optimize performance and reliability
- Orchestrate workflows
- Package evidence for traceable AI usage
- Perform defect and change impact analysis
- Review DHF completeness
- Run compliance checks
- Run hallucination checks
- Support pilot rollouts and feedback loops
- Validate workflows
Perks/Benefits
- N/A
Skills/Tech-stack
Access Control | Audit Logging | Backend Development | Citation Generation | Document chunking | LLM integration | Logging | Machine Learning | Metadata filtering | Observability | Prompt engineering | Prompt versioning | Python | REST API | Retrieval-Augmented Generation | Test automation | Vector Search | Workflow Orchestration
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer | Software Engineer
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