Principal Applied AI Engineering Manager
Tasks
- Build RAG pipelines
- Build and lead AI engineering pod
- Coordinate multi-agent systems
- Define architecture patterns and engineering standards
- Develop evaluation frameworks
- Drive KPIs for volume reduction automation accuracy resolution time and customer satisfaction
- Drive incident response and root cause analysis
- Embed PII protection and audit trails
- Ensure production quality with TDD
- Establish evaluation rubrics and golden datasets
- Hire coach and develop applied AI engineers
- Implement CI CD staged rollouts and observability
- Monitor production health and reliability
- Orchestrate LLM workflows
- Own end to end lifecycle of production AI agents
- Partner with product and platform teams on roadmaps
- Support responsible AI and compliance practices
- Validate agent correctness and safety with judge agents
Perks/Benefits
- N/A
Skills/Tech-stack
Agent Frameworks | Agent systems | Audit trails | Azure | C# | CI/CD | Cause analysis | Code Development | Code platforms | Containers | Data Analysis | Evaluation Frameworks | Incident Response | LLM orchestration | Language Models | Large Language Models | Low Code | Low code development | Microservices | Multi-Agent | Multi-Agent Systems | No Code | No-Code Platforms | Observability | PII protection | Prompt engineering | Python | RAG | Responsible AI | Retrieval-Augmented Generation | Root Cause Analysis | Root cause | TDD
Education
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