JOB DESCRIPTION

Must Have AI Risk Management AWS Minimum Knowledge, Skills and Abilities Required Education and Experience Bachelor s degree in Computer Science, Information Technology, Engineering, Data Science, Mathematics, or a related technical discipline; advanced degree preferred but not required. 9+ years of experience in cloud platform engineering, AI/ML enablement, platform architecture, or enterprise technology solution delivery within secure and governed environments. Experience enabling, supporting, or operationalizing AI/ML and Generative AI platforms, cloud-native services, and enterprise technology capabilities in AWS environments. Hands-on experience with AWS cloud platform services, infrastructure automation, DevOps/IaC practices, and platform enablement frameworks supporting scalable AI and data solutions. Experience collaborating with cross-functional engineering, infrastructure, security, risk, and business teams to deliver enterprise technology and AI enablement capabilities aligned with governance and operational standards.

Technical Skills:

Strong understanding of AWS cloud platform engineering concepts, including provisioning, configuration, automation, monitoring, and operational support of scalable cloud-native environments. Experience enabling and supporting AWS AI/ML and GenAI services such as Amazon Bedrock, SageMaker, Lex, Lambda, API Gateway, S3, IAM, CloudWatch, and related cloud platform capabilities. Proficiency in Infrastructure as Code (IaC), CI/CD automation, and DevOps practices using tools such as Terraform, Jenkins, CloudFormation, GitHub, or similar platform engineering technologies. Strong programming and scripting skills with proficiency in Python and familiarity with SQL, APIs, automation scripting, and cloud integration patterns. Experience enabling Generative AI capabilities including prompt engineering, Retrieval-Augmented Generation (RAG), model orchestration, evaluation frameworks, tool integration, and agentic AI enablement patterns. Knowledge of AI orchestration and frameworks such as LangChain, LlamaIndex, MCPs, vector databases, and enterprise AI integration architectures. Experience designing reusable frameworks, reference architectures, guardrails, and operational standards for secure and governed AI platform enablement. Familiarity with enterprise security, risk, governance, access management, and compliance considerations related to cloud and AI platform operations. Experience supporting AI/ML experimentation, proof of concepts (POCs), and platform enablement activities within controlled enterprise environments. Working knowledge of machine learning concepts, model lifecycle management, observability, and AI evaluation methodologies supporting responsible AI adoption.