AI Engineer

NetBrain Technologies Inc.

. Design and implement core capabilities for an enterprise-grade Agent platform, including orchestration patterns, tool execution, context and memory management, and safety guardrails .

Posted 9/18/2026full-timeToronto • CanadaMid-LevelSenior💰 CA$130,000 - CA$165,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing enterprise-grade Agent platforms and LLM applications, with a strong focus on orchestration, governance mechanisms, and performance optimization. Proficient in building scalable systems and evaluating AI behaviors while ensuring security and reliability in production environments.

Highest-signal resume keywords

LLM Application DevelopmentAgent Architecture DesignPython ProgrammingAPI DevelopmentProduction System Debugging

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringMachine LearningApplied AIAsynchronous ProgrammingTool ExecutionState ManagementError RecoveryLLM Fine-TuningEvaluation System DesignMulti-Step Workflow Development

Soft Skills

CollaborationProblem-SolvingTechnical Leadership

Tools & Technologies

NetBrainGraphRAGKnowledge GraphsLangChainAutoGenLlamaIndexMCPLangSmith

Industry Keywords

Enterprise-Grade SystemsHuman-in-the-Loop WorkflowsPolicy EnforcementSecurity RisksProduction Workloads

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Design and implement core capabilities for an enterprise-grade Agent platform, including orchestration patterns, 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 and implement LLM post-training strategies, preference alignment, and parameter-efficient fine-tuning techniques
Build self-learning feedback loops using production traces, user feedback, and evaluation results
Analyze and optimize LLM behavior, including instruction following, tool calling, structured outputs, contextual understanding, reasoning stability, and hallucination mitigation
Build LLM and Agent evaluation frameworks, automated regression pipelines, quality gates, and hallucination-detection mechanisms
Build AI system observability capabilities including distributed tracing, structured logging, metrics, dashboards, and alerting
Diagnose and resolve production AI failures and unexpected model behavior changes
Design reliable backend services for AI and Agent workloads with asynchronous processing, retries, timeouts, caching, rate limiting, and fault isolation
Optimize latency, throughput, token consumption, and infrastructure cost for large-scale production workloads
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 GraphRAG, Knowledge Graphs, MCP, LLM post-training, and Agent self-learning technologies
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, Knowledge Graphs, GraphRAG, LangGraph, LangChain, AutoGen, LlamaIndex, MCP, LangSmith, LLM fine-tuning, and applying LLM technologies to complex technical domains
Fluent in both English and Chinese

Benefits

Comp & perks

Bonus
RRSP
Medical/dental coverage
Comprehensive benefits package
Reasonable accommodation in the application process