
Apply established machine learning and AI techniques to new problems and datasets. Build, optimize, and maintain machine learning and AI models and supporting pipelines. Evaluate and monitor ML/AI system outcomes, model performance, and data quality. Identify issues in models, pipelines, and datasets; recommend and implement improvements. Design, develop, test, document, refactor, and maintain moderately complex programs/scripts to support ML development and deployment. Follow agreed engineering standards, tools, and best practices to deliver secure, reliable, and maintainable solutions. Collaborate with teammates through code reviews, design reviews, and shared ownership of deliverables. Elicit requirements for ML/AI lifecycle practices, working methods, and automation (e.g., CI/CD, testing, deployment, monitoring). Select and implement appropriate lifecycle practices for components and microservices within the ML/AI solution. Deploy automation to support well-engineered, repeatable, and secure build/release processes. Define ML/AI modules needed for integration builds and produce build definitions for each release/generation of the solution. Validate and accept completed ML/AI modules against agreed functional, quality, and performance criteria. Apply data science techniques to new problems and datasets, using specialized programming approaches where needed. Identify and