Principal AI Engineer
Tasks
- Architect distributed, event-driven systems
- Build ML platforms and pipelines
- Build multi-agent orchestration
- Create AI roadmap with leadership
- Define AI architecture
- Define AI architecture standards
- Deploy and automate model release
- Design and build AI backend services
- Design prompt engineering
- Develop LLM systems
- Develop streaming responses
- Ensure AI reliability monitoring and logging
- Implement RAG systems
- Implement function tool calling
- Integrate AI via APIs
- Lead AI implementation lifecycle
- Lead high stakes technical decisions
- Lead model development and optimization
- Mentor engineering teams
- Orchestrate AI workflows
- Secure deployed AI systems
- Use vector databases for retrieval
Perks/Benefits
- N/A
Skills/Tech-stack
AI Governance | AI Safety | API Design | AWS | Agent systems | Amazon Web Services | Asynchronous processing | Caching | Deployment Automation | Distributed Systems | Docker | Embeddings | Evaluation | Event Driven | Event-driven architecture | Function Calling | Injection Defense | Kubernetes | LLM | Language Models | Large Language Models | MLOps | Model routing | Model versioning | Monitoring | Multi-Agent | Multi-Agent Systems | Observability | PII handling | Prompt engineering | Prompt injection | Prompt injection defense | Python | Quantization | RAG | Responsible AI | Retrieval-Augmented Generation | Streaming | Token Economics | Tool-Calling | Vector Databases | Web Services
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer | Principal | Principal AI Engineer
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