AI Engineer

NetBrain•Burlington, MA

10h•Hybrid

About The Position

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. 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.

Requirements

BS in Computer Science, AI, Electrical Engineering, or related field (advanced degree preferred or equivalent experience)
5+ years of software engineering experience, including building and deploying production-grade LLM, agent, or RAG systems
Proven track record of shipping and supporting AI-powered features in production
Strong Python skills (API design, testing, error handling, environment management)
Working proficiency in C# or C++
Deep understanding of RAG system design and evaluation methodologies
Experience building evaluation frameworks (test sets, metrics, regression pipelines)
Understanding of LLM security risks (prompt injection, data leakage, agent misuse)
Able and willing to commute to our Burlington, MA headquarters twice a week on a hybrid schedule

Nice To Haves

Advanced degree
Experience with graph databases, knowledge graphs, or GraphRAG preferred
Experience designing human-in-the-loop systems and execution controls preferred
Experience with agent frameworks (e.g., LangChain, LangGraph, AutoGen, LlamaIndex)

Responsibilities

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
Design and implement RAG services, including hybrid retrieval, reranking, embeddings, and citation behavior
Define evaluation frameworks and quality metrics to ensure grounded, reliable outputs
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
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
Diagnose and resolve complex AI system issues (retrieval quality, hallucinations, prompt regressions, agent failures)
Lead system design and drive technical decisions through hands-on prototyping and benchmarking