AI Engineer

NetBrain•Burlington, MA

13d•$150,000 - $180,000

About The Position

NetBrain is seeking a Senior AI Engineer to design and build production-grade agent and RAG systems that power intelligent, reliable automation across its platform. This role involves hands-on engineering and system-level thinking, covering architecture, evaluation, scalability, observability, and production reliability. The ideal candidate should be comfortable with ambiguity, capable of moving quickly from prototype to production, and possess a strong focus on quality, safety, and real-world impact.

Requirements

Bachelor's degree or higher in Computer Science, Artificial Intelligence, Electrical Engineering, or a related technical field; equivalent practical experience will also be considered.
3+ years of experience in software engineering, machine learning, or applied AI.
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.
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.
Strong Python and distributed backend engineering skills.
Hands-on experience designing evaluation systems for LLM applications.
Strong understanding of security risks associated with LLM and Agent applications.
Ability to independently design, implement, debug, deploy, and operate complex production systems.

Nice To Haves

Master's or Ph.D. preferred.
Strong experience with RAG and advanced retrieval systems.
Familiarity with Agent frameworks such as LangGraph, LangChain, AutoGen, and LlamaIndex.
Experience designing Agent runtime mechanisms, including Human-in-the-Loop workflows.
Experience with LLM fine-tuning, including LoRA or other parameter-efficient fine-tuning techniques.
Experience applying LLM technologies to networking, infrastructure, cybersecurity, observability, or other complex technical domains.
Fluent in both English and Chinese, with strong cross-regional communication and collaboration skills.

Responsibilities

Design and implement core capabilities for an enterprise-grade Agent platform, including orchestration patterns, 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 integrated with NetBrain platform capabilities and business workflows.
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 like LoRA.
Build an Agent self-learning feedback loop that converts production execution traces, user feedback, and evaluation results into high-quality datasets for continuous improvement.
Analyze and optimize LLM behavior across areas such as instruction following, tool calling, structured output generation, contextual understanding, reasoning stability, and hallucination mitigation.
Build production-grade LLM and Agent evaluation frameworks and automated regression pipelines.
Establish release quality gates and hallucination-detection mechanisms for AI features.
Build comprehensive AI system observability capabilities, including distributed tracing, structured logging, metrics, dashboards, and alerting.
Rapidly diagnose and resolve production AI failures.
Design and implement highly reliable backend services for production AI and Agent workloads.
Continuously optimize latency, throughput, token consumption, and infrastructure cost.
Independently diagnose and resolve complex AI system issues.
Lead technical design for critical modules and system-level capabilities.
Drive technical improvements based on production data, evaluation results, and benchmarks.
Prototype, benchmark, and productionize emerging technologies such as GraphRAG, Knowledge Graphs, MCP, LLM Post-Training, and Agent Self-Learning.
Continuously evaluate Agent frameworks and supporting infrastructure, and provide technical recommendations.
Stay current with developments in LLM and Agent technologies and rapidly translate promising technologies into production-ready capabilities.