Principal AI Software Engineer
Tasks
- Build agentic AI systems
- Build vector retrieval systems
- Conduct code and design reviews
- Coordinate multi-agent workflows
- Define AI platform architecture
- Define APIs and data models
- Develop agent memory and context management
- Enforce AI policies and guardrails
- Establish AgentOps and LLMOps practices
- Implement tool calling services
- Integrate AI agents with enterprise systems
- Integrate Model Context Protocol
- Lead multi-team execution
- Manage state and consistency tradeoffs
- Mentor engineers and raise standards
- Optimize inference for latency and throughput
- Orchestrate LLMs and agents
- Perform tracing monitoring and evals
- Plan rollouts and operational readiness
- Run incident analysis and follow up
- Set SLIs and SLOs
Perks/Benefits
- 401k company match
- Dental insurance
- Health insurance
- Life insurance
- Long-term disability
- Paid Holidays
- Paid parental leave
- Paid sick leave
- Paid time off
- Short-term disability
- Vision insurance
Skills/Tech-stack
AI Governance | APIs | Access Management | Agent memory | Artificial Intelligence | Autogen | Context Management | CrewAI | Databases | Distributed Systems | Docker | Embeddings | Evaluation | Experimentation | Fault Tolerance | Function Calling | GPU Inference | GPU inference optimization | Identity and Access Management | Identity and access | Inference Optimization | Kubernetes | LLM orchestration | Langchain | Langgraph | Language Models | Large Language Models | Llamaindex | Machine Learning | Model Context Protocol | Model Serving | Monitoring | Observability | Prompt engineering | Python | RAG | Regression testing | Retrieval-Augmented Generation | SLIs | SLOs | Secrets management | Security | Tool-Calling | Tracing | Vector Databases
Education
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