Generative AI Solutions Architect

Continue to make an impact with a company that is pushing the boundaries of what is possible. At NTT DATA, we are renowned for our technical excellence, leading innovations, and making a difference for our clients and society. Our workplace embraces diversity and inclusion – it’s a place where you can continue to grow, belong, and thrive.

Your career here is about believing in yourself and seizing new opportunities and challenges. It’s about expanding your skills and expertise in your current role and preparing yourself for future advancements. That’s why we encourage you to take every opportunity to further your career within our great global team.

The Generative AI Solutions Architect designs, develops, and supports AI-enabled applications, workflows, and reusable capabilities while helping teams evaluate and implement AI solutions effectively. This role blends software engineering, AI architecture, and stakeholder consulting to drive secure, scalable, and sustainable AI adoption across the GIN organization.

Want to be a part of our team?

This is a role in our Global IP Network (GIN) division. NTT and the Global IP Network have been recognized as the Best Global Wholesale Carrier (Data) 2021 at the Global Carrier Awards. Our Tier-1 Global IP Network, consistently ranked among the top networks worldwide, spans the Americas, Europe, Asia and Oceania, operating under a single AS 2914.

Key Responsibilities:

• Design, develop, test, deploy, and maintain GenAI-enabled applications, services, APIs, and reusable components for internal platforms and project teams.
• Evaluate and select foundation models and deployment approaches, including open-source and locally hosted models such as Qwen and Ollama-based deployments, using quality, latency, cost, security, privacy, and platform-fit criteria.
• Design and integrate model-driven workflows such as prompting, retrieval-augmented generation (RAG), tool use, and agentic patterns, with appropriate testing, evaluation, guardrails, and fallback behavior.
• Provide early technical input to project teams on feasibility, architecture, model selection, data requirements, GPU capacity, security, responsible AI considerations, performance, and operational risks.
• Create and maintain reference designs, technical documentation, evaluation methods, operating procedures, standards, observability guidance, and recommended usage patterns for GenAI solutions.
• Partner with software, infrastructure, platform, security, and operations teams; educate technical and non-technical stakeholders on GenAI capabilities and limitations; and provide actionable feedback to the Executive, Head - SAS on platform gaps and improvement priorities.
• Develop and maintain reporting at established intervals on significant GIN AI initiatives, activities, and project outcomes. Maintain current GIN AI documentation and guidance, including recommended AI models, core tools, platforms, and other resources available to GIN staff, updating content as organizational needs, technologies, and standards evolve.

Education / Qualifications required:

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related field; or equivalent practical experience.
• Working knowledge of software engineering practices relevant to GenAI solutions, including Python or comparable programming, APIs, source control, testing, containers, and automated deployment practices.
• Working knowledge of GPU compute and memory considerations, foundation-model capabilities and limitations, and open-source or locally hosted model platforms, including tools such as Ollama and models such as Qwen.

Work Experience Required:

• Demonstrated experience designing, building, and supporting GenAI-enabled applications, services, or internal tools in an enterprise or applied engineering environment.
• Demonstrated experience working across software engineering, infrastructure, architecture, security, operations, and business stakeholders to translate use cases into practical technical solutions.
• Demonstrated experience developing and integrating model-driven workflows, including prompting, inference patterns, testing, evaluation, and support practices.
• Demonstrated experience creating technical documentation, implementation guidance, or standards for both technical and non-technical audiences.
• Demonstrated experience working in network services, telecommunications, ma…