Apps AI Solution Architect AMS (Remote Home, US)

Apps AI Architect 539988 Location: North America (Remote) Role Summary The Apps AI Architect will play a pivotal role in transforming how we design, build, and manage enterprise applications in the GenAI era. This role blends deep application architecture expertise — spanning UI/UX, front-end, back-end, integration, data, and cloud — with hands-on AI engineering skills to infuse Artificial Intelligence (including Generative and Agentic AI) across the full application lifecycle — from design and development to modernization and ongoing operations. The Architect will drive innovation, develop reusable AI patterns, and deliver proof-of-value (PoV) initiatives that convert emerging technology possibilities into tangible business impact.

Key Responsibilities

• Architect AI-Native Applications: Design and implement architectures that integrate AI models (LLMs, predictive, and agentic systems) into application workflows to enable reasoning, automation, and contextual decision-making.
• End-to-End Application Design: Lead the design of UI/UX flows, user-facing AI interactions, conversational interfaces, and AI-augmented user journeys across web and mobile applications.
• Drive Modernization Through AI: Reimagine legacy and digital applications by embedding AI capabilities that enable modernization, optimization, and transformation across app portfolios. Design modernization frameworks leveraging AI for architecture discovery, business-rules extraction, and application rationalization. Embed intelligence in re-platformed or refactored applications to create truly AI-native modernization.
• Legacy-to-Modern Mapping: Architect solutions that transform legacy applications into modern Java, .NET, microservices, or cloud-native platforms while preserving core business rules and logic.
• Infuse AI Across Dev & Ops: Partner with delivery and support teams to embed AI in software engineering, testing, incident management, and observability — driving efficiency, resilience, and proactive operations.
• Tooling & Frameworks: Evaluate, integrate, and optimize AI-assisted tools (e.g., code translators, test generators, documentation bots) within modernization pipelines to accelerate delivery.
• Integration & Ecosystem: Define strategies to integrate modernized applications into enterprise ecosystems, including APIs, event-driven architectures, and cloud environments.
• Lead Proofs of Value (PoVs): Design and execute AI-centric PoVs to validate new technologies, tools, and architectures for clients.
• Collaborate & Evangelize: Partner with pre-sales, delivery, and client stakeholders to identify AI opportunities, shape proposals, and articulate the business value of AI-native transformation.
• Develop Reusable Assets: Create frameworks, accelerators, and reference architectures to scale adoption of GenAI and LLM-enabled solutions across multiple accounts. Required Skills & Experience
• 10–15 years of experience in Application Architecture, Engineering, or Digital Transformation, with at least 2–3 years in AI/ML or GenAI implementation.
• Strong experience with Azure OpenAI, OpenAI APIs, Vertex AI, AWS Bedrock, LangChain, LlamaIndex, or similar LLM platforms.
• Deep understanding of modern UI/UX architecture, responsive front-end design, and frameworks such as React, Angular, Vue, or equivalent.
• Proficiency in Python, Node.js, or Java, with exposure to LLM integration, prompt engineering, and API orchestration.
• Experience with leading AI-assisted productivity tools such as Claude, Gemini Code Assist, and GitHub Copilot.
• Familiarity with observability and AIOps platforms including DataDog, Dynatrace, Moogsoft, Splunk AIOps, and ServiceNow AIOps.
• Hands-on exposure to LLM-based ITSM agents and RAG (Retrieval-Augmented Generation) frameworks.
• Experience with MLOps/GenAIOps for continuous model improvement within modernization initiatives.
• Strong background in application modernization (re-platforming, containerization, microservices, and cloud-native design).
• Solid understanding of legacy technologies such as Mainframe, Java, and .NET.
• Knowledge of PromptOps, model observability, AI lifecycle management, and related operational frameworks.
• Excellent communication, stakeholder management, and customer-facing engagement skills.

Preferred Qualifications

• Certifications in AI Engineering (Azure, AWS, or Google) or equivalent credentials.
• Prior experience working in Application Services, AMS, or ADM environments.
• Exposure to agentic workflows, AI observability, or RAG (retrieval-augmented generation) frameworks.
• The role requires active engagement across the full lifecycle — from pre-sales solution shaping through design, development, implementation, and ongoing evolution of AI-native applications.
• Given the evolving nature of AI-native architectures, we welcome candidates who may not meet every requirement but demonstrate strong foundational skills and the ability to grow into the role. Why This Role Matters Thi

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