Join our mission to provide governments with exceptional experiences so they can do the same for their communities!

What do we do?💥

We empower governments to deliver exceptional citizen experiences.

Check out our ‘About Us’page for a deep dive into our product and what makes us exceptional.

CivCheck is transforming how cities manage plan review and building code compliance. As part of Clariti, CivCheck brings together deep domain expertise and cutting-edge AI to streamline and modernize the permitting process. Our Guided AI Plan Review™ (GPR) platform is the first of its kind and it is helping cities and applicants alike navigate complex building codes with clarity, consistency, and speed. By combining intelligent automation with human expertise, CivCheck empowers communities to build faster, safer, and smarter.

How will you help us make an impact? 👩‍💻👨‍💻

The Senior MLOps Engineer will design, build, and scale the systems that power CivCheck and Clariti’s AI capabilities. As the first MLOps Engineer, you will lead the development of robust ML infrastructure, ensuring that models move efficiently from research to production with reliability, observability, and performance at scale.This role is ideal for someone who thrives at the intersection of machine learning, software engineering, and cloud infrastructure, and who’s motivated to enable teams to deliver high-impact ML systems efficiently and safely.

As a Senior MLOps Engineer at Clariti, you’ll get to:

Design and maintain end-to-end ML pipelines for training, evaluation, and deployment of models and agentic AI workflows
Build and optimize infrastructure for training and model serving across cloud providers and compute instances
Develop tools for data pipelines, data labeling, experiment & performance tracking, and automated retraining
Collaborate with AI researchers and ML engineers to productionize POCs and ensure model reproducibility and scalability
Implement CI/CD best practices for ML systems, including continuous integration, automated testing, and deployment workflows
Monitor and manage model health, performance, drift, and data quality in production
Partner with Engineering teams to streamline infrastructure provisioning and data access
Drive cost optimization and performance tuning for large-scale model training
Contribute to internal documentation and best practices

What do you bring to the team? 🧠

6–10+ years of experience in software or ML engineering, with at least 3+ in MLOps or ML infrastructure
Solid experience working with Python, C, C++, Bash, etc
Proven experience deploying and managing ML models in production
Proficiency with Docker, and Kubernetes for scalable ML system design
Experience with cloud platforms (AWS, GCP, or Azure) and GPU orchestration
Hands-on knowledge of CI/CD pipelines (GitHub Actions, Jenkins, or similar)
Familiarity with MLflow, Weights & Biases, Kubeflow, and other similar tools for experiment tracking and pipeline automation
Solid understanding of data versioning, model reproducibility, and monitoring strategies
Excellent problem-solving skills and a collaborative, team-oriented mindset

Bonus Points 🎉

Experience training models from scratch, including defining architectures, curating & cleaning datasets, tuning training parameters, and bringing models from research to monitored production
Exposure to model optimization techniques (quantization, distillation, TensorRT, ONNX)
Familiarity with infrastructure-as-code tools (Terraform, CloudFormation)
Background in distributed systems or high-performance computing
Contributions to open-source projects

What’s in it for you?🫵

We invest in and empower our team members with competitive compensation packages, well deserved time off and benefits to keep you and your family healthy! *

💰 The base salary range for this role is expected to be between $150-200k CAD based on the candidate’s skills, experience, and qualifications while considering internal pay equity and our broader pay philosophy. 💰

If you have questions about compensation as we move through the process, we’re happy to discuss further.

*Benefits depend on employment type (full-time, part-time, contract, etc).

Things to Note 📝

Background checks - Because our customers trust us with sensitive information, we require all successful candidates to undergo comprehensive background checks before joining our team. We focus strictly on global sanctions and criminal offences that are directly relevant to employment at Clariti, and follow all applicable privacy and human rights legislation.

Travel- Although we operate as a remote company, all roles are expected to participate in occasional travel for in-person company-wide or departmental meetings, typically 1-2 times per year. Additional travel requirements specific to the role, if any, will be outlined in the job description.

We're committed to building an inclusive culture where our team members take ownership over projects, tasks, and outcomes; bring a growth mindset to drive continuous learning and self-development; have the ability to communicate courageously in a direct but respectful way; and are customer-focused by keeping the customer at the heart of decision-making. It’s the diversity of our team that helps us make better decisions, by leveraging the diversity in thought & experience across to create impactful solutions as we explore new paths & challenges as we grow. We’re working to create a workplace and team that is as diverse as the communities we serve. We welcome and encourage candidates of all backgrounds to apply.

Questions? We are here to help

If you require accommodations in completing an application, interviewing, completing any pre-employment testing, or otherwise participating in our hiring process for any reason, please direct your questions to [email protected] and we’ll be happy to support you.

Clariti

Senior Machine Learning Operations Engineer

About the Job

Requirements

Responsibilities

Desired Qualifications