Senior AI/ML Quality Assurance Engineer

#LI-LP1

#LI-REMOTE

• Experience validating, testing, or monitoring production machine learning models, particularly classification, regression, or predictive analytics solutions.
• Strong proficiency in Python, SQL, and Git, with hands-on experience handling spatial data structures (e.g., GeoPandas, Shapely, or PostGIS).
• Experience with AI/ML testing, validation, and monitoring frameworks, including automated testing, model performance monitoring, and observability solutions. (e.g. pytest, MLflow, or Deepchecks; Evidently AI, Arize, or cloud observability stacks like GCP Production Monitoring/Cloud Monitoring, Grafana, or Datadog)
• Familiarity with Docker, Kubernetes, and cloud environments (GCP), including application monitoring and production support workflows.
• Ability to translate complex data science and model outputs into clear, actionable documentation for technical and business stakeholders.
• Understanding of model performance measurement, drift detection, and production monitoring concepts.
• Strong technical writing skills with experience creating and reviewing model documentation, validation reports, test plans, runbooks, APIs, or other technical artifacts.
• Ability to collaborate across multidisciplinary teams, including Data Science, MLOps, Software Engineering, Product Management, and Consulting organizations.
• Bachelor's degree.
• 5 years of related experience.

Equivalent additional directly related experience will be considered in lieu of a college degree.

Domestic and/or international travel may be required.

The frequency of travel is contingent on specific duties, responsibilities, and the essential functions of the position, which may vary depending on workload and project demands.

• Bachelor's degree in Computer Science, Software Engineering, Data Science, Geographic Information Systems (GIS), Engineering, or a related quantitative field.
• Experience validating machine learning models, datasets, or analytical solutions within infrastructure, utilities, environmental compliance, water, or wastewater domains.
• Familiarity with geospatial data, spatial analytics, or GIS-based applications.
• Experience working with time-series, sensor, IoT, or SCADA data in operational environments.
• Knowledge of AI governance, model risk management, or responsible AI frameworks.
• Familiarity with cloud-based AI/ML platforms and production monitoring environments.
• Experience evaluating or testing Generative AI, large language model (LLM), or Retrieval-Augmented Generation (RAG) solutions.
• Understanding model explainability, fairness, bias assessment, and AI transparency concepts.