Senior MLOps Engineer, Clariti

• 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

Desirable

• 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
• Design and maintain end-to-end ML pipelines for training, evaluation, and deployment of models and agentic AI workflows
• Build and optimize infrastructure for distributed training and model serving across GPU and cloud environments
• Develop tools for data creation, model versioning, 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