AWS Solutions Architect (AI Implementations) at Look4IT

Job description

This is a remote position. A leading global IT consulting firm is growing its cloud and AI delivery capability to meet rapidly expanding client demand. We are looking for an experienced AWS Solutions Architect to join our expanding AI project teams — working across a portfolio of enterprise AI implementation engagements and helping our clients turn ambitious AI strategies into production-grade cloud reality. You will be the technical authority on AWS architecture within cross-functional delivery squads — designing solutions, guiding engineering teams, and acting as a trusted advisor to client stakeholders. The role spans multiple concurrent projects with varying domains, scales, and compliance requirements, so the ability to bring structure and clarity to complex situations is just as important as deep technical knowledge. WHAT YOU'LL BE DOING: • 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) NICE TO HAVE: • 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

Requirements

Must-haves

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
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

Nice-to-haves

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

Application prep

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Keywords to mirror

aiawsmlapimachine learningllmdockerkubernetes

Platform: zoho-recruit
Effort: medium