Senior MLOps Engineer

Job not on LinkedIn

🔥 5 minutes ago

🌐 United States, Canada – Remote

🇺🇸 United States – Remote 🇨🇦 Canada – Remote

💵 $150k - $200k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Clariti

WebsiteLinkedInAll Job Openings

51 - 200 employees

🏛️ Government

🏢 Enterprise

Government • Enterprise • Software

Clariti is a provider of community development solutions, offering permitting software tailored to governments of all sizes. Their range of products includes solutions for large governments with complex requirements as well as smaller municipalities in need of quick and easy-to-manage systems. Clariti's products are designed with flexibility, allowing users to customize and adjust without deep IT knowledge. They emphasize functionality and support, ensuring that local governments can efficiently serve their communities. Clariti's clientele includes leading governments across North America, demonstrating their capability to deliver robust and reliable permitting systems.

📋 Description

• 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 and 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 • Lead the development of robust ML infrastructure as the first MLOps Engineer, moving models efficiently from research to production with reliability, observability, and performance at scale

🎯 Requirements

• 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 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 • Successful candidates must undergo comprehensive background checks before joining the team • Bonus: Experience training models from scratch, including defining architectures, curating and cleaning datasets, tuning training parameters, and bringing models from research to monitored production • Bonus: Exposure to model optimization techniques (quantization, distillation, TensorRT, ONNX) • Bonus: Familiarity with infrastructure-as-code tools (Terraform, CloudFormation) • Bonus: Background in distributed systems or high-performance computing • Bonus: Contributions to open-source projects

🏖️ Benefits

• Competitive compensation packages • Time off • Benefits to keep you and your family healthy • Occasional travel for in-person company-wide or departmental meetings, typically 1-2 times per year • Inclusive culture and support for accommodations during the hiring process