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 Build production-grade LLM and Agent evaluation frameworks and automated regression pipelines, including benchmark datasets, deterministic checks, LLM-as-a-Judge, tool-call validation, retrieval-quality evaluation, and end-to-end task success metrics. Establish release quality gates and hallucination-detection mechanisms for AI features to prevent significant accuracy, reliability, and performance regressions from reaching production. Build comprehensive AI system observability capabilities, including distributed tracing, structured logging, metrics, dashboards, and alerting. Rapidly diagnose and resolve production AI failures, including hallucinations, incorrect tool selection, invalid tool parameters, Agent loops, retrieval-quality degradation, structured-output failures, latency regressions, and unexpected model behavior changes. Production Engineering and Technical Execution Design and implement highly reliable backend services for production AI and Agent workloads, including asynchronous and concurrent processing, retries, timeouts, caching, rate limiting, and fault isolation. Continuously optimize latency, throughput, token consumption, and infrastructure cost to meet platform SLA requirements and support large-scale production workloads. Independently diagnose and resolve complex AI system issues spanning prompts, models, RAG, tools, Agent workflows, backend services, and infrastructure. Lead technical design for critical modules and system-level capabilities, ensuring solutions align with platform architecture, security requirements, engineering standards, and product requirements. Drive technical improvements based on production data, evaluation results, and benchmarks, and collaborate closely with Engineering, Product, QA, and other teams to deliver solutions into production. Applied Research and Technical Strategy Prototype, benchmark, and productionize emerging technologies such as GraphRAG, Knowledge Graphs, MCP, LLM Post-Training, and Agent Self-Learning to improve grounding, multi-hop reasoning, and domain expertise. Continuously evaluate Agent frameworks and supporting infrastructure, including LangChain, LangGraph, AutoGen, and LlamaIndex, and provide technical recommendations for platform architecture evolution and product technology strategy. Stay current with developments in LLM and Agent technologies and rapidly translate promising technologies into measurable, testable, and production-ready engineering capabilities. What You Bring Bachelor's degree or higher in Computer Science, Artificial Intelligence, Electrical Engineering, or a related technical field. Master's or Ph.D. preferred; equivalent practical experience will also be considered. 3+ years of experience in software engineering, machine learning, or applied AI, including 2+ years building, deploying, and operating production- grade LLM or Agent applications. Must have delivered at least one LLM-powered feature end-to-end and owned its ongoing operation and improvement after production launch. Deep understanding of Agent architectures and LLM behavioral characteristics, including instruction following, tool-calling behavior, and context sensitivity, with hands-on experience building multi-step workflows involving reasoning, tool execution, state management, structured outputs, validation, and error recovery. Proven ability to diagnose and resolve production LLM/Agent failures, including hallucinations,