AI Engineer

NetBrain Technologies Inc.

. Design and implement core capabilities for an enterprise-grade Agent platform, including orchestration patterns such as ReAct, Plan-and-Execute, and Supervisor .

Posted 9/18/2026full-timeBurlington • Massachusetts • United StatesMid-LevelSenior💰 $150,000 - $180,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing enterprise-grade Agent platforms, focusing on orchestration patterns, LLM post-training strategies, and production-grade application deployment. Proficient in building evaluation frameworks, optimizing AI behavior, and ensuring system reliability and security.

Highest-signal resume keywords

LLM Application DevelopmentAgent Architecture DesignPython ProgrammingProduction System DebuggingMulti-Step Workflow Implementation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringMachine LearningApplied AIAsynchronous ProgrammingConcurrent ProcessingAPI DevelopmentEvaluation System DesignTool Execution ManagementState ManagementError Recovery

Tools & Technologies

NetBrain PlatformGraphRAGKnowledge GraphsLLM Fine-TuningLangChainMCPLlamaIndexLangSmithDPO/RLHFLoRA

Industry Keywords

Enterprise-Grade ApplicationsHuman-in-the-Loop WorkflowsPolicy EnforcementSecurity RisksProduction AI FailuresFeedback LoopsHallucination MitigationRelease Quality GatesDistributed TracingStructured Logging

Tech Stack

Tools & technologies

PythonReact

About the role

Key responsibilities & impact

Design and implement core capabilities for an enterprise-grade Agent platform, including orchestration patterns such as ReAct, Plan-and-Execute, and Supervisor
Implement tool execution, context and memory management, and safety guardrails
Design 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 LLM post-training strategies, including domain-specific SFT, DPO/RLHF-based preference alignment, and LoRA fine-tuning
Build self-learning feedback loops using production traces, user feedback, and evaluation results
Analyze and optimize LLM behavior, including instruction following, tool calling, structured output generation, contextual understanding, reasoning stability, and hallucination mitigation
Build LLM and Agent evaluation frameworks and automated regression pipelines
Establish release quality gates and hallucination-detection mechanisms
Build AI observability with distributed tracing, structured logging, metrics, dashboards, and alerting
Diagnose and resolve production AI failures and unexpected model behavior changes
Design reliable backend services with asynchronous and concurrent processing, retries, timeouts, caching, rate limiting, and fault isolation
Optimize latency, throughput, token consumption, and infrastructure cost for platform SLA requirements
Lead technical design for critical modules and system-level capabilities
Collaborate with Engineering, Product, QA, and other teams to deliver production solutions
Prototype, benchmark, and productionize emerging technologies such as GraphRAG, Knowledge Graphs, MCP, LLM post-training, and Agent self-learning
Evaluate Agent frameworks and infrastructure and provide recommendations for platform architecture and product technology strategy

Requirements

What you’ll need

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
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
Ability to diagnose and resolve production LLM/Agent failures
Strong Python and distributed backend engineering skills
Experience with API and service development, asynchronous and concurrent programming, retries, timeouts, caching, rate limiting, testing, logging, and cross-service performance debugging
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
Preferred: experience with RAG, advanced retrieval systems, embeddings, vector and hybrid search, reranking, Knowledge Graphs, GraphRAG, LangGraph, LangChain, AutoGen, LlamaIndex, MCP, LangSmith, LLM fine-tuning, and complex technical domains
Fluent in both English and Chinese

Benefits

Comp & perks

Bonus
401k
Medical coverage
Dental coverage
Reasonable accommodation for disabilities during the application process