Machine Learning Engineer

Validates foundational understanding of AI, ML, and generative AI concepts and responsible use, including selecting appropriate AWS AI services for business use cases.

Assesses knowledge and applied skills for establishing, implementing, and operating AI governance across the AI lifecycle, including organizational governance, applicable laws/standards, development controls, and deployment risk management.

Validates the ability to integrate foundation models into applications and business workflows on AWS, including RAG/vector stores, agentic solutions, safety/governance, optimization, and testing/troubleshooting.

Assesses business-focused knowledge of generative AI concepts and Google Cloud gen AI offerings, including selecting use cases, improving model output, and driving secure and responsible adoption across an organization.

Assesses ability to design, plan, provision, secure, optimize, and operate scalable and cost-effective solutions on Google Cloud using Well-Architected principles.

Assesses ability to design, configure, and operate security controls on Google Cloud across identity and access, network perimeter and segmentation, data protection, security operations, and compliance-aligned governance.

Validates the ability to build, deploy, operationalize, and maintain machine learning solutions and pipelines on AWS, from data preparation through model development, orchestration, monitoring, and security.

Validates foundational knowledge of the AWS Cloud, including cloud concepts, security and compliance, core services and use cases, and AWS billing, pricing, and support resources.

Assesses foundational knowledge of AI and machine learning concepts and how they map to Azure AI services, including responsible AI, computer vision, NLP, and generative AI workloads.