What are the responsibilities and job description for the AWS Cloud Technical Architect - Gen AI position at WinWire?
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