Lead AWS AI Platform Engineer

CGI•Cincinnati, OH

15h•$79,600 - $156,700•Hybrid

About The Position

Make an impact like never before. We're growing rapidly and are looking for a Lead AWS AI Platform Engineer to design, build, and support secure, scalable, and governed AI/ML and GenAI platforms. The engineer will help enable AI experimentation, POCs, model development, and enterprise adoption while ensuring alignment with security, risk, governance, and regulatory standards. This is a hybrid role which requires someone local to Cincinnati, Ohio, willing to be on-site one to two days per week.

Requirements

4+ years of proven experience designing and building AI platforms.
Proven experience building AI capabilities with RAG architecture.
Must have experience leading small teams.
Strong hands-on AWS cloud engineering and platform operations experience.
6+ years of experience in cloud platform engineering, AI/ML enablement, platform architecture, or enterprise technology delivery.
Experience operationalizing AI/ML or GenAI platforms in secure enterprise environments.
Experience working with cross functional teams in regulated or governed environments.
Experience with Bedrock, SageMaker, Lambda, S3, API Gateway, IAM, CloudWatch.
IaC and CI/CD experience using tools such as Terraform, CloudFormation, Jenkins, GitHub
Strong programming/scripting skills, especially with Python.
Familiarity with SQL, APIs, automation scripting, and integration patterns.
Knowledge of AI frameworks and architecture patterns, including: LangChain, LlamaIndex, MCP, Vector databases, and RAG architecture
Understanding of model lifecycle, observability, AI evaluation, and responsible AI practices.
Experience with Docker, Terraform, Git/GitHub, CI/CD pipelines, and DevOps practices.
Bachelor's degree in Computer Science, IT, Engineering, Data Science, Math, or related field.

Responsibilities

Architect and maintain AWS based AI/GenAI platform capabilities.
Enable rapid AI experimentation, POCs, and production ready AI solutions.
Build reusable frameworks, reference architectures, guardrails, and platform standards.
Support AWS AI/ML services
Implement cloud infrastructure using IaC and DevOps practices.
Support structured and unstructured data integration for AI solutions.
Enable GenAI patterns, including prompt engineering, RAG, model orchestration, Agentic AI, and AI evaluation frameworks.
Partner with engineering, security, risk, infrastructure, data, and business teams.
Create technical documentation, governance artifacts, operational procedures, and best practices.
Ensure AI solutions follow responsible AI, security, compliance, and risk standards.