Principal AI Engineer
Tasks
- Architect LLM powered systems prompt engineering and function calling
- Build scalable machine learning platforms pipelines and workflow orchestration
- Co define AI roadmap with leadership
- Define AI architecture and technical strategy
- Define AI architecture standards and champion engineering practices
- Define and advance evaluation and observability practices for LLM apps
- Develop APIs and backend services for AI integrations
- Drive AI safety responsible AI governance and compliance
- Ensure AI reliability security and scalability with logging monitoring and debugging
- Implement RAG architectures with embeddings and vector databases
- Lead AI implementation lifecycle design to production
- Lead model development optimization and production deployment
- Mentor engineers and shape engineering culture
- Orchestrate multi-agent workflows
- Own high stakes architectural decisions
Perks/Benefits
- N/A
Skills/Tech-stack
AI Governance | AI Safety | API Design | AWS | Agent Orchestration | Backend Development | Caching | Deployment Automation | Distributed Systems | Docker | Embeddings | Event Driven | Event-driven architecture | Function Calling | Injection Defense | Kubernetes | Language Models | Large Language Models | Logging | MLOps | Model routing | Model versioning | Monitoring | Multi-Agent | Multi-agent orchestration | Observability | PII handling | Prompt engineering | Prompt injection | Prompt injection defense | Python | Quantization | RAG | Reproducibility | Responsible AI | Retrieval-Augmented Generation | Token Economics | Vector Database
Education
N/A
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