AppModernization_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.