Lead Principal Machine Learning Engineer
Tasks
- Build agentic AI systems
- Build vector retrieval pipelines
- Conduct regression testing and experimentation
- Coordinate multi-agent systems
- Define AI platform architecture
- Design secure API integrations
- Develop agent memory and context management
- Enforce AI policies and guardrails
- Establish AgentOps and LLMOps
- Implement SLIs SLOs and reliability programs
- Implement tool calling services
- Implement tracing monitoring and evaluation
- Integrate Model Context Protocol
- Lead incident response and follow-up
- Lead inference and model serving
- Orchestrate AI workflows
Perks/Benefits
Skills/Tech-stack
Autogen | Cloud infrastructure | CrewAI | Distributed Systems | Docker | Function Calling | GPU efficiency | Inference Optimization | Kubernetes | Langchain | Langgraph | Language Models | Large Language Models | Llamaindex | Machine Learning | Model Context Protocol | Monitoring | Multi-tenant | Observability | Prompt engineering | Python | RAG | Retrieval-Augmented Generation | SLI | SLO | Security | Structured Output | Tool-Calling | Tracing | Vector Databases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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