Jobs in Canada

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Added 1 month ago
AI Engineer
NetBrain
Toronto, ON
Must have delivered at least one LLM-powered feature end-to-end and owned its ongoing operation and
Added 1 month ago
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Senior Software Engineer - Simulation / Développeur(se) logiciel senior – Simulation
Vention
Montreal, QC
- Full-time
Vention is seeking a skilled Software Developer/Engineer for our simulation team who is passionate about developing digital twin experiences for real-world robotics applications. This is an…
Added 1 month ago
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Part-Time Internship - Asset Management Analyst (Students and New Grads)
BGIS
Markham, ON
- Part-time
Plan controlled experiments, perform statistical analysis, and train and validate machine learning and anomaly-detection models for building conditions.
Added 1 month ago
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Team Lead, Senior Fullstack Engineer
Arcadia
Toronto, ON
- $160,000 to $220,000 CAD / year
- Full-time
A fast-growing AI software company is hiring a Team Lead, Senior Full Stack Software Engineer in Toronto to help build a modern enterprise platform used to manage, monitor, and scale AI…
Added 1 month ago
Research Associate (SDL1, Two-year Term)
University of Toronto
Toronto, ON
Supporting and under the guidance of Staff Scientists / Senior Staff Scientists, the Research Associate is expected to carry out and contribute to research projects, by way of their…
Added 1 month ago
GS
Systems Designer
Grantek Systems Integration
Burlington, ON
- Full-time
…design, Network design, Database design, Vision system design, Machine Safety system design, Servo motion control, Information Solution design, MES design, ERP connector development,…
Added 1 month ago
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Systems Designer
Grantek
Burlington, ON
- Full-time
…design, Network design, Database design, Vision system design, Machine Safety system design, Servo motion control, Information Solution design, MES design, ERP connector development,…
Added 1 month ago
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Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Burlington, ON
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
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Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Edmonton, AB
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
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Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Saskatoon, SK
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
D
Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Calgary, AB
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
D
Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Regina, SK
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
D
Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Saint John, NB
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
D
Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Fredericton, NB
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
D
Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Kitchener, ON
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
D
Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Victoria, BC
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
D
Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Halifax, NS
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
D
Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
St. John's, NL · Greater St. John's Metropolitan Area
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
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Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Ottawa, ON
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
D
Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Toronto, ON
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
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Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Moncton, NB
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
D
Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Vancouver, BC
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
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Modern Data Stack Engineer Consultant / Senior Consultant
Deloitte
Winnipeg, MB
- Full-time
Implementing/building transformative use cases on modern data platforms, enabling advanced analytics, machine learning, and AI-driven decisioning.
Added 1 month ago
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Agentic AI Researcher
LG Electronics
Toronto, ON
- Full-time
Work Arrangement: Hybrid: 3 Days per Week On-Site
Added 1 month ago
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Sr. Physical AI Research Scientist
LG Electronics
Toronto, ON
- Full-time
Work Arrangement: Hybrid: 3 Days per Week On-Site

NetBrain

AI Engineer

Toronto, ON, Canada

Apply on job-boards.greenhouse.io About employer

Applications open in a new tab.

Job details

Experience: 3+ years of experience in software engineering, machine learning, or applied AI, including 2+ years building, deploying, and operating production-
Posting language: English
Posted: Sep 18, 2026

Original job description

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, incorrect tool calls, retrieval-quality degradation,
Agent loops, structured-output failures, latency regressions, and regressions introduced by prompt or model changes.
Strong Python and distributed backend engineering skills, including 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, including dataset construction, metric definition, regression testing, and
release quality gates.
Strong understanding of security risks associated with LLM and Agent applications, including prompt injection, data leakage, unsafe tool
execution, permission boundaries, and uncontrolled Agent autonomy.
Ability to independently design, implement, debug, deploy, and operate complex production systems.

Preferred Qualifications

Strong experience with RAG and advanced retrieval systems, including embeddings, vector and hybrid search, reranking, chunking strategies,
grounding and citation mechanisms, as well as multi-hop retrieval, Knowledge Graphs, or GraphRAG.
Familiarity with Agent frameworks such as LangGraph, LangChain, AutoGen, and LlamaIndex, as well as standardized protocols such as MCP
(Model Context Protocol) for connecting Agents with external tools and systems. A strong understanding of the underlying architecture is more
important than expertise in any specific framework.Experience designing Agent runtime mechanisms, including Human-in-the-Loop workflows such as risk classification, approval, interruption, pause
/resume, as well as context management for long-running Agent workflows, including state persistence, history compression, and memory
systems.
Experience with LangSmith or similar LLM observability and evaluation platforms.
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.

What We Offer

Our comprehensive compensation package is vital in how we recognize our people for the impact they make on us reaching our goals as a company.

For this role, the estimated base is CAD $130,000 - CAD $165,000 + Bonus. The actual salary may vary based on a range of factors, including market and individual qualifications objectively assessed during the interview process.

The range listed above is a guideline and may be modified. People Experience offers a comprehensive benefits package in addition to cash compensation that includes but is not limited to RRSP and medical/dental coverage. Speak with your Recruiter for more details on our Total Rewards philosophy.

NetBrain invites all interested and qualified candidates to apply for employment opportunities.

Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, or other characteristics protected by law.

If you have a disability that prevents or limits your ability to use or access the site, or if you require any other accommodation in the application process due to a disability, you may request a reasonable accommodation. To make a request, please contact our People Team at: people@netbraintech.com and we will be happy to assist you.

In compliance with applicable laws, NetBrain conducts holistic, individual background reviews in support of all hiring decisions.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Apply on job-boards.greenhouse.io About employer

More about this job

Responsibilities

Design and build production-grade agent and RAG systems to power intelligent network automation. Develop evaluation frameworks, observability tools, and self-learning feedback loops to ensure reliability and performance in production.

Requirements

Requires a bachelor's degree or higher in a technical field and at least 3 years of experience in software engineering or applied AI. Candidates must have hands-on experience deploying LLM-powered features and strong proficiency in Python and distributed backend engineering.

Skills

Python
LLM
Agent architectures
RAG
Machine learning
Distributed systems
Fine-tuning
Prompt engineering
Evaluation frameworks
Observability
API development
Cloud computing
Data structures
Software engineering
System architecture
Networking

Benefits

RRSP
Medical coverage
Dental coverage

Visa sponsorship

Not detected in the job text

Keywords

AI Engineer
No-code
Network automation
Hybrid multi-cloud
Agent platform
RAG
LLM
ReAct
Supervised Fine-Tuning
DPO
RLHF
LoRA
GraphRAG
Knowledge Graphs
LangChain
LangGraph
AutoGen
LlamaIndex
MCP
Python
Distributed systems
Observability
Latency optimization
Prompt engineering
Hallucination mitigation
API development
Cybersecurity
Infrastructure
Data engineering
Software engineering

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Work requirements

Jobs in Canada