Application Modernization AWS Solution Architect

Overview The primary responsibility of this role is to act as an AWS Agentic Product Engineer in medium-to-large opportunities for mid-sized clients; that may involve solutioning including but not limited to understanding client requirements, designing reusable AI assets, developing agentic workflows, engineering cloud-native AI products, and establishing scalable deployment frameworks. Mid-market organizations increasingly require AI enabled products that can be deployed rapidly, reused across multiple engagements, and adapted to changing business requirements. The role requires the ability to combine software engineering, cloud infrastructure, automation, testing, and AI innovation to create differentiated solutions. Key Responsibilities Solution Engineering, Product Strategy & Asset Development Translate business and technical requirements into reusable AI-enabled product assets using AWS-native services and modern engineering practices. Collaborate with Sales Pursuit, Solution Architects, Engineering, and Delivery Teams to develop scalable and repeatable AI offerings. Design and build reusable agentic AI-enabled solutions that accelerate deployment and adoption across multiple client engagements. Drive innovation through proof-of-concepts, pilot solutions, and packaged AI assets. AWS Agentic Product Architecture Design reusable agent configurations, orchestration frameworks, and AI workflow architectures. Build scalable infrastructure-as-code templates and deployment accelerators using AWS services. Define reference architectures supporting agentic AI, intelligent automation, and AI native applications. Integrate AI products with enterprise systems, APIs, data platforms, and cloud services. Optimize architecture patterns for reusability, maintainability, and deployment efficiency. Engineering Excellence, Testing & Quality Assurance Develop robust evaluation, testing, validation, and monitoring frameworks for AI enabled products. Implement automated testing approaches that ensure quality, reliability, and performance consistency. Establish engineering standards, coding practices, and governance frameworks for reusable assets. Support product lifecycle management, version control, release management, and continuous improvement. Identify opportunities to improve engineering productivity through automation and reusable accelerators. Client Engagement & Collaboration Partner with architects, delivery teams, product owners, and business stakeholders to align solutions with business objectives. Participate in discovery sessions, architecture reviews, and product roadmap discussions. Provide technical advisory support for reusable AI products and cloud-native solution architectures. Communicate engg decisions, product capabilities, and solution benefits to stakeholders. Thought Leadership & Innovation Contribute to AI product innovation strategies and reusable asset development initiatives. Mentor engineering teams on AI product development, cloud engineering, and agentic architecture principles. Contribute to reusable frameworks, accelerators, best practices, and thought leadership activities. Experience Minimum 5–8 yrs exp in Product Engineering, S/W Engineering, Cloud Enggg, AI Engg, or related domains. Experience designing and building cloud-native apps and reusable software assets on AWS. Experience developing AI, machine learning, automation, or intelligent workflow solutions, infrastructure-as-code, CI/CD pipelines, and automated deployment frameworks. Demonstrated ability to translate business requirements into scalable product architectures. Experience developing proof-of-concepts, reusable accelerators, testing frameworks, and productized assets. Experience supporting enterprise AI transformation and cloud modernization initiatives.Education Bachelor s degree or equivalent minimum 12 years of work experience . Associate degree with at least 6 years of relevant work experience. Preferred Skills Deep understanding of AWS cloud services, cloud native architectures, and software engineering practices. Experience building agentic AI solutions, intelligent automation platforms, and reusable AI assets. Strong knowledge of infrastructure as code, CI CD, DevOps, testing automation, and cloud security. Understanding of Generative AI, orchestration frameworks, intelligent agents, and evaluation methodologies. Experience designing scalable, maintainable, and reusable product architectures. Strong communication, collaboration, and stakeholder management skills. Ability to lead complex engineering initiatives across cross functional teams. Strong analytical, troubleshooting, and problem solving capabilities. The candidate should have PE VC mentality. Bonus Points Proven track record building reusable AI products and cloud native engineering assets. Experience developing agentic AI frameworks, evaluation accelerators, and deployment templates. Experience with Agile, DevOps, MLOps, and modern engineering operating models. Knowledge of AWS Bedrock, intelligent agents, automation frameworks, and AI orchestration platforms. Contributions to reusable frameworks, patents, publications, innovation programs, or thought leadership initiatives.

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