Apps AI SOLUTION ARCHITECT at Atos

, Oregon, United States -

Full Time

Start Date

Immediate

Expiry Date

21 Feb, 26

Salary

0.0

Posted On

23 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Application Architecture, AI Engineering, UI/UX Design, Python, Node.js, Java, MLOps, Cloud-Native Design, Legacy Technologies, AI Lifecycle Management, Observability, Generative AI, Microservices, Integration, Data, Cloud

Industry

Business Consulting and Services

Description

Apps AI Architect 539995 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 This role is central to our AI-Native Application Services transformation. The Apps AI Architect will directly shape how enterprise applications evolve — blending the power of AI, data, and cloud to create intelligent, adaptive, and self-improving systems that redefine how our clients build, run, and scale their businesses.

Responsibilities

The Apps AI Architect will design and implement AI-native application architectures, integrating AI models into workflows for enhanced decision-making. They will also lead modernization efforts for legacy applications and drive innovation through reusable AI patterns.