AWS Cloud & AI Platform

VSG Business Solutions•Cincinnati, OH

About The Position

We are seeking a senior-level AWS Cloud & AI Platform Engineer to help build, enable, govern, and advance enterprise Artificial Intelligence and Generative AI capabilities within a highly secure and regulated banking environment. This role sits at the intersection of AWS cloud engineering, AI/ML platform architecture, Generative AI enablement, security, governance, and AI risk management. The successful candidate will help create the technical foundation that allows development and business teams to safely experiment with, develop, evaluate, and ultimately operationalize AI solutions across the enterprise. This is not solely an AI application-development or data-science position. The role requires a strong platform engineering mindset and hands-on experience designing secure, scalable, reusable AWS environments and services that support enterprise AI adoption. The engineer will work across engineering, infrastructure, information security, risk, data, architecture, and business teams to enable AI capabilities while maintaining the governance, resiliency, security, and operational controls expected within a major financial institution.

Requirements

Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, Mathematics, or a related technical discipline.
9+ years of professional technology experience involving cloud engineering, platform engineering, AI/ML enablement, architecture, or enterprise technology delivery.
Strong hands-on experience architecting, engineering, and supporting AWS cloud environments.
Demonstrated experience with AI Risk Management, AI governance, Responsible AI, or implementing technical controls around enterprise AI usage.
Experience enabling, supporting, or operationalizing AI/ML or Generative AI platforms in enterprise environments.
Experience designing secure, scalable, governed cloud infrastructure.
Strong understanding of enterprise security, governance, access management, operational risk, and compliance requirements.
Strong Python
Strong SQL
Strong REST APIs
Strong Automation scripting
Strong Cloud integration patterns

Nice To Haves

AWS Amazon Bedrock
AWS Amazon SageMaker
AWS Lambda
AWS S3
AWS API Gateway
AWS IAM
AWS CloudWatch
AWS networking, security, monitoring, and platform services
Generative AI / AI Engineering
Generative AI
Large Language Models
Retrieval-Augmented Generation
Prompt engineering
AI/model orchestration
Agentic AI
AI evaluation frameworks
Model monitoring
Guardrails
Responsible AI
Model lifecycle management
AI Frameworks & Integration
LangChain
LlamaIndex
Model Context Protocol (MCP)
Vector databases
Enterprise APIs
Structured and unstructured data integration
DevOps / Platform Engineering
Terraform
CloudFormation
Jenkins
GitHub
CI/CD pipelines
Infrastructure as Code
Platform automation
Development
The strongest candidate will combine three disciplines: 1. AWS Platform Engineering Deep hands-on experience designing, automating, securing, and operating enterprise AWS environments. 2. AI/GenAI Platform Enablement Experience establishing reusable AI infrastructure and services that allow engineering teams to build and deploy AI capabilities efficiently. 3. AI Risk Management & Governance Practical experience implementing the controls, guardrails, evaluation processes, monitoring, security, and governance required to safely operate AI within a regulated enterprise. Experience simply developing an AI application or RAG chatbot will not, by itself, demonstrate sufficient fit. The ideal candidate has helped establish the enterprise platform, architecture, governance, and operational framework behind AI solutions.

Responsibilities

Architect, implement, maintain, and enhance secure and scalable AWS-based AI and Generative AI platform capabilities.
Design shared infrastructure and reusable platform services supporting AI/ML development, experimentation, proof-of-concepts, and production deployment.
Enable AWS AI/ML and cloud-native services including: Amazon Bedrock, Amazon SageMaker, AWS Lambda, Amazon S3, API Gateway, IAM, CloudWatch, and related AWS AI, data, security, and integration services.
Create reusable architectures and platform patterns that accelerate AI adoption across multiple engineering teams.
Build and maintain highly automated AWS environments using Infrastructure as Code and CI/CD practices.
Improve platform scalability, resilience, observability, security, developer experience, and operational efficiency.
Help establish and operationalize enterprise AI risk management and responsible AI controls.
Develop technical guardrails and governance mechanisms for the secure and responsible use of AI and Generative AI technologies.
Support model and solution evaluation processes addressing: Output quality, Accuracy, Reliability, Security, Privacy, Responsible AI usage, Model behavior, Monitoring and observability.
Create governance frameworks supporting model validation, AI solution approval, ongoing monitoring, and risk management.
Partner with Security, Risk, Compliance, Architecture, and Engineering teams to ensure AI capabilities align with enterprise policies and regulatory expectations.
Establish technical controls around access management, data protection, model usage, auditing, monitoring, and platform governance.
Enable enterprise Generative AI capabilities using technologies and architectural patterns such as: Large Language Models, Amazon Bedrock, Retrieval-Augmented Generation (RAG), Prompt engineering, Model orchestration, AI agents and agentic workflows, Tool integration, Model evaluation frameworks, Vector databases, Enterprise knowledge integration.
Support frameworks and technologies such as LangChain, LlamaIndex, MCP, vector databases, and related AI orchestration technologies.
Design scalable approaches for integrating structured and unstructured enterprise data into AI solutions.
Develop patterns for connecting AI platforms with APIs, data sources, applications, enterprise systems, and external services.
Evaluate emerging AI technologies and determine their suitability for enterprise adoption.
Provision and manage AWS infrastructure using Infrastructure as Code technologies such as: Terraform, AWS CloudFormation, and similar enterprise IaC technologies.
Develop automated deployment and platform-management processes through CI/CD pipelines.
Work with technologies such as: GitHub, Jenkins, Terraform, CloudFormation, Python, APIs, Automation scripting.
Create repeatable deployment frameworks that improve consistency, security, reliability, and speed of platform delivery.
Develop and maintain: Reference architectures, Engineering standards, Reusable platform patterns, Technical guardrails, Operational procedures, Architecture documentation, AI governance artifacts.
Establish best practices for AI experimentation, platform development, solution deployment, monitoring, and production readiness.
Support platform modernization and continuously evaluate opportunities to improve the enterprise AI ecosystem.
Lead technical investigations, experiments, and proof-of-concepts involving emerging AI/ML and Generative AI technologies.
Evaluate new AWS and AI capabilities for potential enterprise adoption.
Help determine where emerging technologies can improve developer productivity, platform scalability, AI quality, security, or operational efficiency.
Translate successful experimentation into reusable enterprise platform capabilities.
Work closely with: Cloud and Platform Engineering, Application Development, Data Engineering, Information Security, Enterprise Architecture, Risk and Compliance, Infrastructure, Product teams, Business stakeholders.
Provide technical leadership and guidance regarding AWS AI platform architecture and responsible AI adoption.
Communicate complex technical and AI risk concepts clearly to technical and non-technical stakeholders.
Operate with a high degree of ownership and accountability within agile delivery teams.