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NetBrain

AI Engineer

Burlington, MA | HybridmidAdded today

About this role

NetBrain seeks a Senior AI Engineer to design and build production-grade agent and RAG systems for intelligent network automation. You'll own architecture, evaluation, scalability, and reliability—moving quickly from prototype to production while maintaining a focus on safety and real-world impact.

What you'll do

  • Design and implement enterprise-grade agent platform architecture with orchestration patterns (ReAct, Plan-and-Execute, Supervisor) and safety guardrails
  • Build agent execution and governance mechanisms including human-in-the-loop workflows, multi-tenant isolation, and policy enforcement
  • Develop LLM post-training strategies using SFT, DPO/RLHF, and parameter-efficient techniques (LoRA) for domain-specific optimization
  • Create reusable agent skills, standardized tool interfaces, and a scalable tool ecosystem integrated with NetBrain platform
  • Implement self-learning feedback loops converting production traces and user feedback into training data
  • Ensure production reliability, observability, scalability, and quality across all AI systems

What they're looking for

  • Agent system design and orchestration patterns
  • Large language models and prompt engineering
  • Fine-tuning techniques (SFT, DPO, RLHF, LoRA)
  • RAG (Retrieval-Augmented Generation) systems
  • Python and production engineering practices
  • System architecture and scalability design
  • Network operations domain knowledge (preferred)
  • Evaluation frameworks and metrics for AI systems
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NetBrain

NetBrain builds a network automation platform powered by AI agents and retrieval systems designed for enterprise network management. The company is hiring senior engineers focused on developing robust, scalable AI systems and solving complex network automation challenges.

View all jobs at NetBrain

Likely interview questions

  • Walk us through your experience designing agent systems—what orchestration patterns have you implemented and why did you choose them?
  • How would you approach building a self-learning feedback loop that converts production execution traces into high-quality training data?