AWS Solutions Architect (AI Implementations)

This is a remote position.

• Designing end-to-end AWS architectures for AI and GenAI implementations across multiple client projects
  
• Leading architecture discovery workshops and Well-Architected Framework reviews with client teams
  
• Selecting and configuring the right AWS AI/ML services for each use case — Bedrock, SageMaker, Comprehend, Textract, and beyond
  
• Defining IaC standards and reviewing infrastructure implementations by engineering teams
  
• Advising on data architecture — ingestion, storage, transformation, and access patterns optimised for AI workloads
  
• Working with client security and compliance teams to ensure architectures meet regulatory and enterprise requirements
  
• Contributing to pre-sales and solutioning — proposals, effort estimates, and client technical presentations where needed
  
• Mentoring cloud engineers on the delivery team and conducting architecture and code reviews
  
• Staying current with the AWS AI/ML service roadmap and advising clients on relevant emerging capabilities
  

Requirements

• Proven AWS architecture experience at senior or lead level (5+ years hands-on, across multiple production environments)
  
• Hands-on experience designing and implementing AI/ML solutions on AWS — Amazon SageMaker, Amazon Bedrock, or equivalent managed AI services
  
• Deep knowledge of AWS core services: compute (EC2, ECS, EKS, Lambda), storage (S3, EFS), databases (RDS, DynamoDB, Aurora), networking (VPC, API Gateway, CloudFront)
  
• Experience with Infrastructure as Code — Terraform, AWS CDK, or CloudFormation — with a production track record, not just familiarity
  
• Understanding of data architecture patterns for AI workloads: data lakes, streaming pipelines, feature stores, vector databases
  
• Security-first design mindset — IAM, VPC design, encryption, compliance with GDPR and common enterprise security frameworks
  
• Ability to work directly with clients: translating business requirements into architectural decisions and communicating them clearly to both technical teams and senior stakeholders
  
• English proficiency at B2 or above (working language across international delivery teams and clients)
  

• AWS certification — Solutions Architect Professional, Machine Learning Specialty, or equivalent (valued but not a hard requirement)
  
• Experience with GenAI application architecture: LLM integration, RAG (Retrieval-Augmented Generation), agentic AI frameworks (LangChain, LangGraph) running on AWS infrastructure
  
• MLOps experience — model deployment pipelines, monitoring, drift detection, model versioning (SageMaker Pipelines, MLflow, etc.)
  
• Multi-account AWS architecture: AWS Organizations, Control Tower, landing zones
  
• Cost optimisation expertise — FinOps practices, Reserved Instances, Savings Plans, rightsizing for AI/GPU workloads
  
• Experience in regulated industries: financial services, healthcare, or public sector — with associated compliance requirements
  
• Containerised AI workloads: Docker, Kubernetes (EKS), Helm
  
• Observability and monitoring: CloudWatch, AWS X-Ray, OpenTelemetry