AWS Solutions Architect (AI Implementations)
这是一个远程职位。一个领先的全球 IT 咨询公司正在扩大其云和 AI 交付能力，以满足快速增长的客户需求。我们寻找一名经验丰富的 AWS Solutions Architect 来加入我们的不断扩大的 AI 项目团队在企业人工智能实施承诺组合中工作并帮助我们的客户将雄心勃勃的 AI 战略转化为生产级云现实。你将成为跨功能交付小组内的 AWS 架构技术权威设计解决方案，指导工程团队，并担任客户利益相关者可信的顾问。这项角色涵盖了多个同时项目，具有不同领域，规模和合规要求，因此为复杂情况带来结构和清晰度的能力与深入技术知识一样重要。你将做什么：· 设计用于多个客户项目 AI 和 GenAI 实现的端到端 AWS 架构 · 与客户团队进行建筑发现研讨会和精确架构框架审查 · 为每个使用案例选择和配置正确的 AWS AI/ML 服务 Bedrock,SageMaker,Comprehend,Textract 及其他领域 · 定义 IAC 标准并通过工程团队对基础设施实施进行评估 · 咨询数据架构 用于人工智能工作负载的摄入，存储，转换和访问模式；· 与客户安全与合规团队合作以确保建筑物符合监管和企业要求；· 在需要时为预售和解决方案做出贡献；· 提供服务人员技术介绍；· 导师云工程师在交付团队中进行结构和代码审查；· 保持最新 AWS AI/ML 服务路线图并对相关新兴能力提供顾问建议。·在 AWS 设计和实施 AI/ML 解决方案的实践经验亚马逊 SageMaker,Amazon Bedrock 或同等管理 AI 服务 ·深入了解 AWS 核心服务：计算 (EC2,ECS,EKS,Lambda),存储 (S3,EFS),数据库 (RDS,DynamoDB,Aurora),网络 (VPC,API Gateway,CloudFront) ·使用基础设施的经验作为代码 Terraform,AWS CDK 或 CloudFormation 与生产记录，而不仅是熟悉 ·理解人工智能工作模式的数据架构模式
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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
Originally posted on Himalayas