Cloud Engineer

Job Description

AWS Cloud Engineer

Logistics

Client: Fifth Third Bank

Location: Hybrid – Cincinnati, OH or Detroit, MI

Duration: 12-Month Contract-to-Hire (CTH)

The AWS AI Platform Engineer is responsible for enabling and advancing enterprise AI and machine learning capabilities within a secure, governed, and scalable technology environment across the Bank. This role architects, implements, and maintains AI enablement platforms and services that support rapid experimentation, proof of concepts (POCs), model development, and enterprise adoption of GenAI and AI/ML solutions.

The role is accountable for enabling AI services, maintaining cloud and platform environments, supporting structured and unstructured data integrations, and driving innovation through experimentation, technical exploration, and emerging technology evaluations. Responsibilities include designing scalable AI architectures, supporting platform modernization, creating reusable enablement capabilities, and ensuring alignment with enterprise technology strategy, operational resiliency, and regulatory requirements.

Essential Duties and Responsibilities

AI Platform Enablement, Governance, and Innovation

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• Enable, architect, and support secure, governed, and scalable AI/GenAI platform capabilities that accelerate enterprise AI adoption across the Bank.
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• Design, implement, and maintain AI enablement environments, services, frameworks, and reusable architectural patterns to support rapid experimentation, proof of concepts (POCs), and production readiness.
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• Architect, provision, and maintain AWS cloud-based AI platform infrastructure and shared services using Infrastructure as Code (IaC), automation pipelines, and cloud engineering best practices to ensure scalability, resiliency, and operational efficiency.
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• Enable and support AWS AI/ML and cloud-native services such as Amazon Bedrock, SageMaker, S3, Lambda, API Gateway, and related platform capabilities within secure and governed enterprise environments.
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• Partner with cross-functional teams including engineering, infrastructure, security, risk, data, and business stakeholders to onboard and enable AI solutions aligned with enterprise governance and technology standards.
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• Evaluate, enable, and operationalize emerging AI/ML and Generative AI technologies, services, and platforms for enterprise use cases and strategic initiatives.
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• Establish and maintain AI platform standards, reference architectures, guardrails, and best practices for secure and responsible AI development and deployment.
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• Support the enablement and integration of structured and unstructured data sources, cloud-native AI services, APIs, and enterprise platforms to facilitate scalable AI solution delivery.
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• Stay current with emerging AI/ML technologies, AWS cloud technologies frameworks, and methodologies.
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• Contribute to establishing best practices for AI exploration and deployment.
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Collaboration and Communication

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• Create and maintain technical documentation, architectural guidance, operational procedures, and governance artifacts related to AI enablement capabilities and platform services.
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• Lead technical exploration, innovation initiatives, and technology evaluations to identify opportunities for improving AI platform capabilities, developer experience, scalability, and operational efficiency.
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• Develop evaluation and governance frameworks to support model validation, responsible AI usage, monitoring, observability, and output quality assessments.
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• Collaborate with agile squads, platform engineering teams, and product stakeholders to deliver enabling capabilities and technical solutions aligned with enterprise AI strategy and business priorities.
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• Stay current with emerging AI/ML technologies, cloud platform services, architectural trends, and industry best practices to continuously evolve the Bank’s AI enablement ecosystem.
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• Deliver platform enablement and technical capabilities for defined product areas or strategic initiatives while operating with a high degree of ownership, collaboration, and operational accountability.
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Minimum Knowledge, Skills and Abilities Required

Education and Experience

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• Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, Mathematics, or a related technical discipline; advanced degree preferred but not required.
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• 9+ years of experience in cloud platform engineering, AI/ML enablement, platform architecture, or enterprise technology solution delivery within secure and governed environments.
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• Experience enabling, supporting, or operationalizing AI/ML and Generative AI platforms, cloud-native services, and enterprise technology capabilities in AWS environments.
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• Hands-on experience with AWS cloud platform services, infrastructure automation, DevOps/IaC practices, and platform enablement frameworks supporting scalable AI and data solutions.
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• 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.
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Technical Skills

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• Strong understanding of AWS cloud platform engineering concepts, including provisioning, configuration, automation, monitoring, and operational support of scalable cloud-native environments.
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• 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.
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• 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.
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• Strong programming and scripting skills with proficiency in Python and familiarity with SQL, APIs, automation scripting, and cloud integration patterns.
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• Experience enabling Generative AI capabilities including prompt engineering, Retrieval-Augmented Generation (RAG), model orchestration, evaluation frameworks, tool integration, and agentic AI enablement patterns.
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• Knowledge of AI orchestration and frameworks such as LangChain, LlamaIndex, MCPs, vector databases, and enterprise AI integration architectures.
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• Experience designing reusable frameworks, reference architectures, guardrails, and operational standards for secure and governed AI platform enablement.
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• Familiarity with enterprise security, risk, governance, access management, and compliance considerations related to cloud and AI platform operations.
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• Experience supporting AI/ML experimentation, proof of concepts (POCs), and platform enablement activities within controlled enterprise environments.
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• Working knowledge of machine learning concepts, model lifecycle management, observability, and AI evaluation methodologies supporting responsible AI adoption.
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