Job Description, Responsibilities & Requirements
About the Position
We are seeking an experienced AI Infrastructure Architect with deep expertise in designing and operating scalable, secure, and high-performance cloud environments for Generative AI and LLM workloads. This role is ideal for someone who combines strong AWS architectural skills with hands-on experience in GPU compute, MLOps/LLMOps, and enterprise-grade AI platform design.
You should bring extensive experience building cloud-native AI infrastructure, optimizing large-scale model training and inference environments, and collaborating closely with AI/ML teams to enable advanced GenAI capabilities.
Responsibilities
- Design and implement scalable AWS infrastructure to support Generative AI and LLM workloads, including training, fine-tuning, and inference.
- Architect secure, high-performance environments using AWS core services such as Amazon SageMaker, Amazon Bedrock, Amazon EKS, AWS Lambda, and related cloud-native components.
- Design GPU-based compute environments (e.g., EC2 P-series, G-series) optimized for distributed training, fine-tuning, and low-latency inference.
- Implement secure VPC architectures, private endpoints, IAM policies, encryption (KMS), and enterprise-grade data governance controls.
- Build and govern MLOps/LLMOps pipelines using SageMaker Pipelines, CodePipeline, and CI/CD best practices.
- Architect RAG infrastructure, including vector databases (OpenSearch, Aurora PostgreSQL with pgvector) and scalable storage solutions (S3).
- Establish monitoring and observability using CloudWatch, model monitoring tools, logging frameworks, and performance dashboards.
- Optimize infrastructure for latency, autoscaling, high availability, and cost efficiency, leveraging Spot Instances, Savings Plans, and right-sizing strategies.
- Define disaster recovery (DR) and backup strategies across multi-AZ and multi-region AWS setups.
- Implement Infrastructure as Code (IaC) using Terraform or CloudFormation for consistent, repeatable provisioning of AI environments.
- Collaborate with AI/ML teams to support LLM fine-tuning, prompt orchestration, inference endpoints, and model deployment workflows.
- Stay current with AWS GenAI advancements, evaluating new services, architectural patterns, and best practices for enterprise adoption.
Requirements
Must have
- Extensive experience (typically 7+ years) in cloud architecture, infrastructure engineering, or platform engineering, with a strong focus on AWS.
- Proven expertise designing and operating AI/ML and Generative AI infrastructure at scale.
- Deep knowledge of AWS services relevant to AI workloads (SageMaker, Bedrock, EKS, EC2 GPU instances, Lambda, VPC, IAM, KMS, S3).
- Hands-on experience with GPU compute, distributed training, and high-performance inference environments.
- Strong understanding of MLOps/LLMOps practices, CI/CD pipelines, and model deployment workflows.
- Experience architecting secure, compliant, and highly available cloud environments.
- Proficiency with Infrastructure as Code (Terraform or CloudFormation).
- Familiarity with vector databases, RAG architectures, and scalable data storage patterns.
- Strong collaboration skills and the ability to work closely with AI/ML, DevOps, and engineering teams.
- Excellent documentation and communication skills.
Nice to have
We Offer
- Competitive salary
- Opportunity to work in a dynamic and innovative environment
- Professional development opportunities
About the Company
[Company description if present]
Location
London, United Kingdom of Great Britain and Northern Ireland
Application
Apply for AI Infra Architecture in London
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