AWS Fullstack Architect

JD for AWS Full stack: Design and architect end-to-end solutions for complex business problems, considering scalability, performance, security, and cost-effectiveness. Lead the design and implementation of microservices-based architectures, defining service boundaries, APIs, and interaction patterns. Architect and integrate Generative AI and Machine Learning models into applications, defining data pipelines, model deployment strategies, and inference mechanisms. Design and develop Agentic AI solutions that autonomously perform tasks across business processes. Build and orchestrate workflows using LangGraph and integrate with AWS services. Leverage Amazon Bedrock to create and deploy intelligent models for document processing, prediction, and classification. Collaborate with data scientists, ML engineers, and development teams to translate models into production-ready, scalable solutions. Define and enforce architectural standards, patterns, and best practices across application development teams. Evaluate and select appropriate technologies, frameworks, and tools for application development, microservices, AI/ML integration, and cloud deployment. Design and optimize solutions for deployment on at least one major cloud platform (AWS, Azure, or GCP), leveraging relevant cloud services (e.g., compute, storage, databases, AI/ML services, networking). Provide technical leadership and guidance to development teams throughout the software development lifecycle. Create and maintain technical documentation, including architectural diagrams, design specifications, and technical guidelines. Collaborate with stakeholders, including product managers, business analysts, and other architects, to understand requirements and translate them into technical designs. Stay abreast of the latest trends and advancements in microservices, GenAI, Machine Learning, cloud computing, and web technologies. Drive innovation by identifying opportunities to leverage new technologies and approaches to improve our applications and processes. Assess and mitigate technical risks. Support the implementation of DevOps and MLOps practices for seamless deployment and management of applications and models.