Description

• 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
  • 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.

AI Risk Management & Responsible AI

• 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.

Generative AI Enablement

• 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.

Cloud Automation & DevOps

• Provision and manage AWS infrastructure using Infrastructure as Code technologies such as:
  • Terraform
  • AWS CloudFormation
  • 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.

Architecture, Standards & Platform Governance

• 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.

Innovation & Technical Exploration

• 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.

Collaboration

• 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.

Required Qualifications

• 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.

Technical Qualifications

Candidates should demonstrate strong experience across several of the following areas:

AWS

• Amazon Bedrock
• Amazon SageMaker
• Lambda
• S3
• API Gateway
• IAM
• 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

• Strong Python
• SQL
• REST APIs
• Automation scripting
• Cloud integration patterns

Ideal Candidate Profile

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

Key Skills

Education

Bachelor's degree