AWS Cloud Technical Architect - Gen AI

Job Type :

Full-Time / Contract with

WinWire Job Description :

We are seeking an experienced Technical Architect with deep expertise in AWS Cloud architecture and Generative AI (GenAI) solutions. The ideal candidate will design and lead the implementation of scalable, secure, and production-grade cloud architectures while integrating advanced GenAI capabilities such as LLM-powered applications, RAG systems, and AI-driven automation. This role requires strong architectural vision, hands-on AWS experience, and a solid understanding of modern AI ecosystems. Design end-to-end Cloud-native architectures on AWS aligned with business and technical requirements Architect and implement Generative AI solutions using LLMs, RAG pipelines, and AI orchestration frameworks Lead technical design discussions and produce high- and low-level architecture documentation Define best practices for scalability, security, reliability, and cost optimization Design multi-account AWS environments using Well-Architected principles Integrate AI services into enterprise systems and microservices architectures Provide technical leadership and mentorship to engineering teams Establish governance, data security, and responsible AI practices Evaluate emerging GenAI technologies and recommend appropriate solutions Collaborate with stakeholders, product managers, and DevOps teams to deliver robust solutions Experience Range - 10-12 Years; B.E/B.Tech Graduate in the field of Technology Strong collaboration and communication skills to work effectively with diverse teams and stakeholders

Skills & Experience :

5+ years of strong experience in AWS which should include below services - AWS Lambda, Amazon EC2, Amazon

ECS / EKS, S3, RDS

/ Aurora, DynamoDB, Amazon API Gateway, AWS Step Functions, OpenSearch, Bedrock, AWS

IAM, AWS

CloudFormation / Terraform and CloudWatch Experience working with LLMs and GenAI platforms (e.g., OpenAI, Anthropic, open-source LLMs) Cost optimization and performance tuning; Experience with serverless and Microservices-based architectures; Multi-region deployment and high-availability design; Hands-on with Amazon Bedrock and managed AI services; RAG (Retrieval-Augmented Generation) architecture design Vector databases (e.g., OpenSearch, Pinecone, FAISS); Prompt engineering and evaluation frameworks AI model integration into enterprise applications; Understanding of AI security, guardrails, and compliance