Lead Principal Machine Learning Engineer
San Francisco, CA, United States
USD 169K-355K Senior-level Full Time
Tasks
- Build agentic AI systems
- Build vector retrieval systems
- Coordinate multi-agent workflows
- Create eval suites and regression testing
- Define OCI AI platform architecture
- Design inference and model serving
- Develop agent memory and context management
- Enforce policies and safety guardrails
- Establish AgentOps and LLMOps observability
- Implement tool calling services
- Integrate Model Context Protocol
- Integrate enterprise APIs and cloud services
- Lead multi team execution plans
- Mentor engineers through code reviews
- Orchestrate LLM agents
- Run incident analysis and follow up
- Set SLIs and SLOs
Perks/Benefits
Skills/Tech-stack
Access Control | Agent systems | AgentOps | Auditability | Autogen | Batching | Caching | Cloud infrastructure | CrewAI | Docker | Embeddings | Evaluation | Function Calling | GPU Inference | Human-in-the-loop | Incident Response | Inference Optimization | Kubernetes | LLM | LLMOps | Langchain | Langgraph | Llamaindex | Long Context | Model Serving | Monitoring | Multi-Agent | Multi-Agent Systems | Multi-tenant | Multi-tenant architecture | Observability | Oracle Cloud | Oracle Cloud Infrastructure | Prompt engineering | Python | Quantization | RAG | Regression testing | Retrieval-Augmented Generation | SLI | SLO | Security | Structured outputs | Tenant architecture | The Loop | Tool-Calling | Tracing | Vector Databases
Education
Regions
Countries
States
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