AI Engineer

Founded in 2004, NetBrain is the leader in no-code network automation. Its ground-breaking Next-Gen platform provides IT operations teams with the ability to scale their hybrid multi-cloud connected networks by automating the processes associated with Diagnostic Troubleshooting, Outage Prevention and Protected Change Management. Today, over 2,500 of the world’s largest enterprises and managed services providers leverage NetBrain’s platform.

What We Need

We’re looking for a Senior AI Engineer to design and build production-grade agent and RAG systems that power intelligent, reliable automation across our platform. This role combines hands-on engineering with system-level thinking—owning everything from architecture and evaluation to scalability, observability, and reliability in production. The ideal candidate thrives in ambiguity, moves quickly from prototype to production, and brings a strong focus on quality, safety, and real-world impact.

What You'll Do

Agent Platform & Architecture

• Design and build core agent platform capabilities, including orchestration patterns (e.g., ReAct, Plan-and-Execute), tool execution layers, memory/context management, and guardrails
• Develop reusable agent skills and a standardized tool ecosystem aligned with platform conventions
• Implement enterprise-grade controls for agent execution, including human-in-the-loop workflows, permissioning, and policy enforcement

Retrieval & AI Systems

• Design and implement RAG services, including hybrid retrieval, reranking, embeddings, and citation behavior
• Define evaluation frameworks and quality metrics to ensure grounded, reliable outputs

Production Engineering & Reliability

• Build automated evaluation and regression pipelines to maintain quality at scale
• Establish observability (tracing, metrics, alerting) and optimize systems for latency, throughput, and cost
• Define best practices and incident response processes to ensure production readiness

Applied Research & Technical Strategy

• Prototype and productionize emerging approaches (e.g., GraphRAG, knowledge graphs, MCP integrations)
• Evaluate AI frameworks and infrastructure (e.g., LangChain, LangGraph, AutoGen, LlamaIndex) and guide platform decisions

Technical Execution

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