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 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