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Applied Ai Engineer Jobs in Quebec (NOW HIRING)

Norbert Health

Applied AI Engineer

Montreal, QC · On-site

The position We're looking for an Applied AI Engineer to take our growing collection of foundation models and ML components from manually run, sometimes locally trained workflows to fully automated ...

Norbert Health

Applied AI Engineer

Montreal, QC

The position We're looking for an Applied AI Engineer to take our growing collection of foundation models and ML components from manually run, sometimes locally trained workflows to fully automated ...

Simpro Software

Applied AI Engineer

Montreal, QC

Contexte du poste The Applied AI Software Engineer will be responsible for the rapid technical design and delivery of AI agents and frameworks built on top of SIMPRO FSMs. Working closely with the AI ...

PSP Investments

Senior Applied AI Engineer

Montreal, QC · On-site +1

As a Senior Applied AI Engineer , you'll: * Design and build the core of Floyd's agentic system: prompting, tool use, retrieval across unstructured and structured data, multi-model orchestration and ...

Ironbelly Studios

AI Platform Engineer

Montreal, QC · Hybrid

This is an Applied AI Engineering Position To ensure we're connecting with the right candidates, let's be clear about the focus of this role: * ✅ We ARE looking for: A seasoned backend/platform ...

Autodesk

Senior Applied AI/ML Developer

Montreal, QC · On-site

Job Requisition ID # 26WD99788 L'affichage de poste en francais suivra / The French job posting follows 26WD99788 Sr. Applied AI/ML Developer Position overview As a Senior ML Developer on the team ...

Autodesk

Software Architect, AI/ML

Montreal, QC

The Applied AI team in Autodesk's Data and Process Management (DPM) organization ships Cloud-Native ... Partner closely with Product, Security/Privacy, and Engineering leaders to deliver high-impact ...

Broccolini

Senior AI Skill builder

Kirkland, QC · On-site

Experience avec les LLMs, le prompt engineering ou les outils AI workflows * A l'aise dans un ... Continuously improve outputs through iteration and prompt refinement What We're Looking For Applied ...

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Applied Ai Engineer information

What are the key skills and qualifications needed to thrive as an applied AI engineer?

To thrive as an Applied AI Engineer, you need strong proficiency in programming (especially Python), machine learning algorithms, statistics, and a relevant degree in computer science or a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience with cloud platforms (such as AWS or Azure), and knowledge of data management tools are typically required. Excellent problem-solving, communication, and teamwork skills help you translate complex models into real-world solutions and collaborate across disciplines. These competencies ensure you can effectively develop, deploy, and maintain AI systems that drive business value.

What are some common challenges applied AI engineers face when deploying AI models into production environments?

Applied AI Engineers often encounter challenges such as ensuring models perform consistently on real-world data, optimizing models for speed and scalability, and integrating AI solutions with existing systems. Managing data privacy, monitoring for model drift, and maintaining robust documentation are also key concerns. Collaboration with DevOps, data engineering, and product teams is essential to address these challenges effectively and deliver reliable AI-driven solutions.

What is the difference between Applied Ai Engineer vs Data Scientist?

AspectApplied Ai EngineerData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; experience with AI frameworksBachelor's or Master's in CS, Statistics, or related fields; strong analytical skills
Work EnvironmentDevelops and deploys AI models in production environmentsAnalyzes data to extract insights and build predictive models
Industry UsageUsed in tech, healthcare, finance for deploying AI solutionsUsed across industries for data analysis and modeling

Applied Ai Engineers focus on implementing and deploying AI models in real-world applications, while Data Scientists primarily analyze data to generate insights and build predictive models. Both roles require similar educational backgrounds but differ in their core responsibilities and work environments.

How much does an applied AI engineer make?

An applied AI engineer's salary varies based on experience, location, and industry, but typically ranges from $80,000 to $150,000 annually. Senior roles or those with specialized skills in machine learning, deep learning, and programming languages like Python or TensorFlow tend to earn higher salaries.

What does an applied AI engineer do?

An applied AI engineer develops and implements artificial intelligence models and algorithms to solve real-world problems. They work with data, machine learning frameworks, and programming languages like Python or TensorFlow to create practical AI solutions for businesses or products.

What job categories do people searching Applied Ai Engineer jobs in Quebec look for?

The top searched job categories for Applied Ai Engineer jobs in Quebec are:

Applied Ai Engineer jobs near you
Infographic showing various Applied Ai Engineer job openings in Quebec as of August 2026, with employment types broken down into 77% Full Time, 20% Part Time, and 3% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution.

Applied AI Engineer

Montreal, QC • On-site

Norbert Health
Fitness and Sports Centers • 11 - 50 employees
Learn more about Norbert Healthexternal

Full-time

Medical, Vision

Re-posted 28 days ago

Job description

The company

Norbert is building autonomous robots that deliver healthcare.

Our AI sensing platform enables existing robotic platforms to become care team members: rounding on patients, capturing vitals without contact (FDA-cleared for pulse and respiratory rate, more in the pipeline), running assessments, documenting to the EMR, and escalating when something's wrong. Autonomously.

We're not building demos. We're deployed in real facilities today, monitoring hundreds of patients daily. We're solving one of healthcare's hardest problems: a global nursing shortage that will hit 40% by 2030.

We're a small, international team backed by top-tier VCs, with offices in Brooklyn, Paris, and Montreal. We ship things that matter.

The position

We're looking for an Applied AI Engineer to take our growing collection of foundation models and ML components from manually run, sometimes locally trained workflows to fully automated, production-grade MLOps pipelines: deployed reliably on robots in nursing facilities. We need someone who knows the model landscape cold, treats evaluation as a first-class engineering problem, and has strong opinions about when to prompt, RAG, fine-tune, swap, or buy.

You'll work across cloud and edge deployments, and some of the systems you'll touch are on a SaMD pathway, so you'll need to be comfortable shipping under regulatory constraints.

What you'll do
  • Integrate foundation models and ML components (VLMs, LLMs, ASR/TTS, detection/segmentation, embeddings) into our production pipelines, using both open-weight models and third-party APIs
  • Build RAG and agent-style orchestration for clinical reporting and conversational interfaces
  • Ship real-time streaming pipelines (voice agents) alongside batch and request-response workloads
  • Build evaluation harnesses that catch regressions across model swaps and measure performance against clinical-grade accuracy targets
  • Fine-tune and retrain models (LoRA, PEFT, supervised fine-tuning) using data collected from our deployed fleet
  • Deploy across our inference surfaces: third-party APIs, self-hosted, and on-robot edge
  • Build the data flywheel: pipelines that collect, label, version, and feed production data back into model improvement
  • Partner with the algorithms team (signal processing, computer vision) on integration with their lower-level pipelines
What we're looking for
  • BS in Computer Science, Engineering, or a related field, or equivalent hands-on experience
  • 4+ years shipping ML/AI systems in production outside of academic settings
  • Strong working knowledge of the modern foundation model landscape (open-weight LLMs and VLMs, common detection/segmentation backbones, embedding models)
  • Hands-on experience with PEFT/LoRA and supervised fine-tuning
  • Strong Python; comfortable with the deployment toolchain (ONNX, quantization, at least one inference runtimeTensorRT, vLLM, llama.cpp, etc.)
  • Experience with a cloud ML training/MLOps platform (GCP Vertex AI, AWS SageMaker, Azure ML, or equivalent)
  • Ability to work independently, solve complex problems, and drive projects to completion
Bonus points
  • Edge ML deployment (Jetson, ARM, mobile NPUs)
  • Real-time voice AI pipelines (STT, TTS, streaming LLM)
  • Production RAG systems beyond toy implementations
  • Medical devices, SaMD, or other regulated ML environments
  • MLOps tooling (Weights & Biases, MLflow, DVC, etc.)
  • Active learning or human-in-the-loop labeling workflows
  • C++ for integrating with our computer vision pipeline
What we offer
  • Real impact: your code provides care for patients today
  • High autonomy and technical ownershipyou'll define how we operate AI in production
  • Work at the intersection of cutting-edge AI, edge computing, and healthcare
  • A talented, excellent, diverse and international team
  • Equity participation in the company's future
  • Cutting-edge stack: embedded AI, robotics, LLMs, multimodal sensing
  • Transparent, mission-driven culture focused on continuous learning
  • Competitive salary and equity