Senior MLOps Engineer
Clariticloudinc · US (Remote)
About this role
**Senior MLOps Engineer** Join our mission to provide governments with exceptional experiences so they can do the same for their communities! **About CivCheck (Clariti)** 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 helps cities and applicants navigate complex building codes with clarity, consistency, and speed—combining intelligent automation with human expertise to build faster, safer, and smarter. --- ## How will you help us make an impact? As the **Senior MLOps Engineer**, you’ll design, build, and scale the systems that power CivCheck and Clariti’s AI capabilities. As the first MLOps Engineer, you’ll lead the development of robust ML infrastructure—ensuring models move efficiently from research to production with reliability, observability, and performance at scale. --- ## 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 (continuous integration, automated testing, 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 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 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 (architectures, dataset curation/cleaning, tuning, research-to-production) - Exposure to model optimization (quantization, distillation, TensorRT, ONNX) - Familiarity with infrastructure-as-code (Terraform, CloudFormation) - Background in distributed systems or high-performance computing - Contributions to open-source projects --- ## Compensation & benefits - Expected base salary range: **$150–200k CAD**, based on skills, experience, and qualifications (with internal pay equity considerations) - Benefits depend on employment type (full-time, part-time, contract, etc.) --- ## Things to note 📝 - **Background checks:** Required for successful candidates (focused on global sanctions and criminal offences directly re
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