AWS Cloud & AI Platform Engineer II(onsite, only W2)

• 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.