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 implement core capabilities for an enterprise-grade Agent platform, including orchestration patterns such as ReAct, Plan-and-
• Execute, and Supervisor, as well as tool execution, context and memory management, and safety guardrails.
• Design enterprise-grade Agent execution and governance mechanisms, including Human-in-the-Loop approval workflows, multi-tenant
• permission isolation, policy enforcement, and secure execution controls.
• Build reusable Agent Skills, standardized tool interfaces, and a scalable tool ecosystem deeply integrated with NetBrain platform
• capabilities and business workflows.

LLM and Model Optimization

• Design and implement LLM post-training strategies, including domain-specific Supervised Fine-Tuning (SFT), DPO/RLHF-based
• preference alignment, and parameter-efficient fine-tuning techniques such as LoRA, to continuously improve model performance in the
• network operations domain.
• Build an Agent self-learning feedback loop that converts production execution traces, user feedback, and evaluation results into high-
• quality datasets for continuously improving prompts, skills, models, and retrieval strategies.
• Analyze and optimize LLM behavior across areas such as instruction following, tool calling, structured output generation, contextual
• understanding, reasoning stability, and hallucination mitigation.

Evaluation, Reliability, and Observability