AI/LLM Application Engineer
Tasks
- Add source citation mechanisms
- Build enterprise read only integrations
- Build prompt pipelines and RAG services
- Create reviewer facing outputs
- Detect traceability gaps
- Develop AI LLM application services
- Generate draft evidence outputs
- Implement AI agents for requirement quality checks
- Implement audit logging model routing access control and project isolation
- Implement compliance checks
- Implement retrieval pipelines metadata schemas and templates
- Implement workflow orchestration and evidence generation
- Maintain prompt libraries and version management
- Maintain technical documentation developer documentation and configuration records
- Optimize performance, reliability, and observability
- Perform defect change impact analysis
- Review DHF completeness
- Run prompt regression tests and hallucination checks
- Support OQ PQ test harness and validation evidence
- Support pilot rollouts and user feedback loops
- Validate workflow outputs
Perks/Benefits
- N/A
Skills/Tech-stack
API | Access Control | Audit Logging | Authentication | Authorization | Backend Development | Cloud Computing | Data Processing | Document chunking | Evaluation | Hallucination detection | Logging | Metadata filtering | Model APIs | Observability | Orchestration | Prompt engineering | Python | RAG | Retrieval Pipeline | Structured data | Testing | Vector Search | Workflow automation
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer | Software Engineer
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