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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AWS Cloud & AI Platform Engineer II - **Company:** Everforth Apex - **Location:** Cincinnati, OH, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Computing Platforms, Architectural Patterns, Cloud Engineering, Continuous Integration, Data Integration, DevOps, Python (Programming Language), Machine Learning, Cloud Services, Azure Machine Learning, SQL Databases, Enterprise Software Applications, Cloud Platform System, Delivery Pipeline, Multi-Agent Systems, Generative AI, Infrastructure as Code (IaC), Cloudformation, AI Platforms, Infrastructure Automation Frameworks, Information Technology, Functional Programming, Api Gateway, Terraform, Serverless Computing, Jenkins - **Published:** September 10, 2026 - **Apply:** https://www.dice.com/job-detail/492fd5da-4943-4b9d-9469-f027de310f34 ## About the Role Education: Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, Mathematics, or a related technical discipline. An advanced degree is preferred but not required. Experience: 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 is required. Candidates should have hands-on experience with AWS cloud platform services, infrastructure automation, and DevOps/IaC practices. Technical Skills: Proficiency in Infrastructure as Code (IaC), CI/CD automation, and DevOps practices using tools such as Terraform, Jenkins, or CloudFormation. Strong programming and scripting skills with proficiency in Python and familiarity with SQL and APIs. Experience with AWS AI/ML services like Amazon Bedrock and SageMaker is necessary. Knowledge of AI orchestration frameworks such as LangChain and LlamaIndex is also required. ## Description 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. This role architects, implements, and maintains AI enablement platforms and services that support rapid experimentation, proof of concepts, model development, and enterprise adoption of GenAI and AI/ML solutions. The position is accountable for enabling AI services, maintaining cloud and platform environments, and supporting data integrations., * Enable, architect, and support secure, governed, and scalable AI/GenAI platform capabilities to accelerate enterprise AI adoption. * Design, implement, and maintain AI enablement environments, services, frameworks, and reusable architectural patterns. * Architect, provision, and maintain AWS cloud-based AI platform infrastructure and shared services using Infrastructure as Code (IaC) and automation pipelines. * Enable and support AWS AI/ML and cloud-native services such as Amazon Bedrock, SageMaker, S3, Lambda, and API Gateway. * Partner with cross-functional teams including engineering, infrastructure, security, risk, and data stakeholders. * Evaluate, enable, and operationalize emerging AI/ML and Generative AI technologies for enterprise use cases. * Establish and maintain AI platform standards, reference architectures, guardrails, and best practices. * Support the integration of structured and unstructured data sources, cloud-native AI services, and APIs. * Create and maintain technical documentation, architectural guidance, and operational procedures. * Collaborate with agile squads, platform engineering teams, and product stakeholders to deliver enabling capabilities. ## Related Videos - [Infrastructure as Code: The Developer's Secret Weapon](https://www.wearedevelopers.com/videos/1221-infrastructure-as-code-the-developer-s-secret-weapon) - [The Road to MLOps: How Verivox Transitioned to AWS](https://www.wearedevelopers.com/videos/1050-the-road-to-mlops-how-verivox-transitioned-to-aws) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Implementing Feature Environments with AWS and Terraform](https://www.wearedevelopers.com/videos/531-implementing-feature-environments-with-aws-and-terraform) - [Our GitOps approach for deploying an Identity Provider and an API Gateway in a SaaS company](https://www.wearedevelopers.com/videos/776-our-gitops-approach-for-deploying-an-identity-provider-and-an-api-gateway-in-a-saas-company) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)