Platform Engineer

Description: On-site in Cincinnati, OH Our client seeks an AWS AI Platform Engineer to enable and advance enterprise AI and machine learning capabilities within a secure, governed, and scalable environment. The role will architect, implement, and maintain AI enablement platforms and services that support rapid experimentation, proof of concepts, model development, and enterprise adoption of Generative AI and AI/ML solutions. The position will enable AI services, maintain cloud and platform environments, integrate structured and unstructured data, and drive innovation through experimentation and technical evaluations. The engineer will design scalable AI architectures, support platform modernization, create reusable enablement capabilities, and ensure alignment with enterprise technology strategy, resiliency, and regulatory requirements. Due to client requirements, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance. Rate: $80.00 to $88.00/hr. w2 Responsibilities: Enable, architect, and support secure, governed, and scalable AI and GenAI platform capabilities that accelerate enterprise AI adoption. Design, implement, and maintain AI enablement environments, services, frameworks, and reusable patterns for experimentation, POCs, and production readiness. Architect, provision, and maintain AWS cloud-based AI platform infrastructure and shared services using IaC, automation pipelines, and cloud engineering best practices. Enable and support AWS AI/ML and cloud-native services such as Amazon Bedrock, SageMaker, S3, Lambda, API Gateway, and related capabilities within secure and governed environments. Partner with engineering, infrastructure, security, risk, data, and business stakeholders to onboard and enable AI solutions aligned with governance and standards. Evaluate, enable, and operationalize emerging AI/ML and Generative AI technologies, services, and platforms for enterprise use cases. Establish and maintain AI platform standards, reference architectures, guardrails, and best practices for secure and responsible AI development and deployment. Support enablement and integration of structured and unstructured data sources, cloud-native AI services, APIs, and enterprise platforms for scalable delivery. Create and maintain technical documentation, architectural guidance, operational procedures, and governance artifacts for AI enablement and platform services. Lead technical exploration, innovation initiatives, and technology evaluations to improve platform capabilities, developer experience, scalability, and efficiency. Develop evaluation and governance frameworks to support model validation, responsible AI usage, monitoring, observability, and output quality. Collaborate with agile squads, platform engineering teams, and product stakeholders to deliver enabling capabilities aligned with enterprise AI strategy. Deliver platform enablement and technical capabilities for defined product areas or strategic initiatives with ownership and operational accountability. Experience Requirements: 9+ years in cloud platform engineering, AI/ML enablement, platform architecture, or enterprise technology within secure and governed environments. Experience enabling, supporting, or operationalizing AI/ML and GenAI platforms and cloud-native services in AWS. Hands-on experience with AWS services, infrastructure automation, DevOps/IaC, and platform enablement frameworks supporting scalable AI and data solutions. Strong understanding of AWS platform engineering including provisioning, configuration, automation, monitoring, and operations of cloud-native environments. Experience with Amazon Bedrock, SageMaker, Lex, Lambda, API Gateway, S3, IAM, CloudWatch, and related capabilities. Proficiency with IaC and CI/CD using tools such as Terraform, Jenkins, CloudFormation, GitHub, or similar. Proficiency in Python and familiarity with SQL, APIs, automation scripting, and cloud integration patterns. Experience enabling Generative AI capabilities including prompt engineering, RAG, model orchestration, evaluation frameworks, tool integration, and agentic 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 for cloud and AI operations. Experience supporting AI/ML experimentation, POCs, and platform enablement in controlled enterprise environments. Working knowledge of ML concepts, model lifecycle management, observability, and AI evaluation methodologies. Education Requirements: 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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