Lead Principal AI Software Engineer
Tasks
- Build agent execution and inference services
- Conduct code reviews design reviews and incident follow up
- Define AI platform architecture for agentic systems
- Define APIs data models and state management
- Design secure multi tenant AI operations
- Establish AgentOps and LLMOps monitoring tracing and guardrails
- Implement model serving and evaluation pipelines
- Integrate AI agents with enterprise APIs and cloud services
- Integrate MCP and vector retrieval
- Lead multi team technical strategy and execution plans
- Mentor engineers and raise architectural standards
- Optimize inference for latency throughput GPU utilization cost
- Orchestrate LLM workflows and tool calling
- Own reliability performance security and cost outcomes
- Set SLIs and SLOs and production readiness criteria
Perks/Benefits
- 401k matching
- Disability insurance
- Employee stock purchase plan
- Flexible spending accounts
- Life insurance
- Medical, dental, and vision insurance
- Paid Holidays
- Paid parental leave
- Paid sick leave
- Paid time off
Skills/Tech-stack
Access Control | AgentOps | Audit Logging | Autogen | Batching | Caching | Cloud infrastructure | Context Management | CrewAI | Docker | Embeddings | Experimentation | Function Calling | Human-in-the-loop | Identity Systems | Incident Response | Inference Optimization | Kubernetes | LLMOps | Langchain | Langgraph | Language Models | Large Language Models | Llamaindex | Long Context | Memory systems | Model Serving | Monitoring | Observability | Prompt engineering | Python | Quantization | RAG | Regression testing | Retrieval-Augmented Generation | SLI | SLO | Secrets management | Security | Service Level | Service-Level Objectives | Structured Output | The Loop | Tool-Calling | Tracing | Vector Databases
Education
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