Senior MLOps Engineer

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

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.

Civ Check is transforming how cities manage plan review and building code compliance.
As part of Clariti, Civ Check 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, Civ Check empowers communities to build faster, safer, and smarter.

The Senior MLOps Engineer will design, build, and scale the systems that power Civ Check 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 s 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.

• 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 product ionize 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

• 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 (Git Hub 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
• 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, Cloud Formation)
• 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…