Empowering governments to deliver exceptional citizen experiences by designing, building, and scaling systems that power CivCheck and Clariti's AI capabilities
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 other similar tools for experiment tracking and pipeline automation
Responsibilities
- 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
Other
- Background checks are required for all successful candidates
- Occasional travel for in-person company-wide or departmental meetings, typically 1-2 times per year
- Bachelor's, Master's, or Ph.D. degree in a relevant field (not explicitly mentioned but implied)
- Excellent problem-solving skills and a collaborative, team-oriented mindset
- Ability to communicate courageously in a direct but respectful way
