Principal Machine Learning Engineer
Tasks
- Build agentic AI systems
- Build evaluation frameworks
- Coordinate multi-agent systems
- Define OCI AI platform architecture
- Define SLIs and SLOs
- Design APIs and data models
- Develop model serving services
- Drive incident response and follow up
- Enforce policies and safety guardrails
- Establish AgentOps and LLMOps practices
- Establish observability and tracing
- Implement vector retrieval
- Integrate MCP
- Lead technical design and code reviews
- Maintain reliable secure cost aware operations
- Manage state and consistency tradeoffs
- Mentor engineers
- Optimize GPU inference and training
- Orchestrate LLM tool calling workflows
- Secure integrations with enterprise systems
Perks/Benefits
- 401k savings plan with company match
- Health care and dependent care flexible spending accounts
- Life Insurance and AD D
- Medical, dental, and vision insurance
- Paid parental leave
- Paid sick leave
- Paid time off
Skills/Tech-stack
Access Control | Agent systems | AgentOps | Auditability | Autogen | Batching | Caching | Context Management | CrewAI | Distributed Systems | Docker | Embeddings | Fault Tolerance | Function Calling | GPU Inference | High Throughput | Identity Management | Incident Response | Kubernetes | LLMOps | Langchain | Langgraph | Language Models | Large Language Models | Llamaindex | Low Latency | Memory Management | Model Serving | Model routing | Monitoring | Multi-Agent | Multi-Agent Systems | OCI | Observability | Policy enforcement | Policy governance | Prompt engineering | Python | Quantization | RAG | Retrieval-Augmented Generation | SLI | SLO | Security | Structured Output | Tool-Calling | Tracing | Vector Databases
Education
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