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Planitar

MLOps Engineer

Waterloo, ON, N2L 5Z4, Canada

Full-time

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Job details

Job type: Full-time
Posting language: English
Posted: Oct 8, 2026

Original job description

ABOUT US

At Planitar, we build technology that helps people see and understand the world’s spaces more clearly.

We are the creators of iGUIDE, a powerful property technology solution that combines immersive 3D tours, accurate floor plans and reliable spatial data into a single, easy-to-use system. Our cameras and software platform are used by professionals in real estate, insurance, construction and property management to simplify their work and deliver better results.

Planitar recently joined REA Group, one of the world’s leading digital property companies. REA operates Australia’s largest residential and commercial property platforms, including realestate.com.au and has a growing global footprint across Asia and North America. Together, we are shaping the future of property technology, combining Planitar’s deep spatial data expertise with REA’s global scale and innovation engine.

OUR VISION

A connected future where reliable spatial data enables informed decisions across the property lifecycle.

OUR VALUES

Integrity: We do the right thing even when it’s hard.
Teamwork: We are one team, on a big mission.
Innovation: We value creative solutions for emerging opportunities.
Accountability: We honour our commitments.

ABOUT THE ROLE

Planitar's Advanced Research Group builds the AI that turns 360° scans into floor plans. We are hiring our first MLOps Engineer to build the infrastructure that trains, evaluates, deploys and monitors our floor-plan generation and automated drafting QA models. You will own this platform end to end.

RESPONSIBILITIES

Build an automated evaluation harness that scores every new model and gates releases on regressions.
Automate and version the data generation and training pipelines.
Set up experiment tracking and a model registry.
Package and deploy models into our desktop drafting application and automated pipeline, with independent rollout and rollback.
Operate LLM-based agents in production: prompt and model versioning, cost and latency tracking.
Monitor model quality, drift and cost in production.
Manage our GPU compute and CI/CD for ML.

QUALIFICATIONS

4+ years in MLOps, ML platform or ML-focused software engineering, with models you took to production.
Strong Python; able to work with C++ codebases.
PyTorch training workflows and ONNX export.
Experiment tracking, model registries and data versioning (e.g. MLflow, W&B, DVC).
Docker, workflow orchestration and CI/CD.
AWS and Linux GPU environments.
Comfortable as the sole owner of a platform on a small research team.

NICE TO HAVE

LLMOps or agent evaluation experience.
Computer vision, 360° imagery or point-cloud data.
Shipping ML inside desktop or edge applications.

PERKS AND BENEFITS

100% employer-paid flexible benefits plan
Comprehensive Health & Dental coverage
Health Spending Account (HSA) or Lifestyle Spending Account (LSA) options
3% DPSP match
Hybrid Work Environment
Professional Development Incentive
Opportunities to grow as we scale
The backing of a global industry leader through REA Group

ACCESSIBILITY, INCLUSION & AI DISCLOSURE

Planitar is committed to a diverse, equitable, inclusive workplace and welcomes applications from all backgrounds. This is a back fill position. We provide accommodation under AODA at any hiring stage upon request. In line with Ontario law, we use AI only to summarize interview notes, never to screen, assess, or decide on candidates - all decisions are made by our team. Please note this is a new position.

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More about this job

Responsibilities

You will build and own the end-to-end infrastructure for training, evaluating, deploying, and monitoring floor-plan generation and automated drafting QA models. This includes managing GPU compute, CI/CD pipelines, and operating LLM-based agents in production.

Requirements

The role requires 4+ years of experience in MLOps or ML-focused software engineering with models deployed to production. Candidates must be proficient in Python, PyTorch, and cloud-based GPU environments, with the ability to work independently on a research team.

Skills

MLOps
Python
C++
PyTorch
ONNX
MLflow
W&B
DVC
Docker
CI/CD
AWS
Linux
GPU compute
LLMOps
Computer vision

Benefits

Flexible benefits plan
Health coverage
Dental coverage
Health spending account
Lifestyle spending account
DPSP match
Professional development incentive

Visa sponsorship

Not detected in the job text

Keywords

MLOps
Machine Learning
Python
C++
PyTorch
ONNX
MLflow
Weights & Biases
DVC
Docker
CI/CD
AWS
Linux
GPU
LLMOps
Computer Vision
3D Scans
Floor Plans
Spatial Data
Infrastructure
Model Registry
Experiment Tracking
Data Pipelines
Automation
QA Models
Agent Evaluation
Point-cloud data
Edge applications
Desktop applications
Property Technology

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