[Hiring] Generative AI Solutions Architect @NTT Ltd.

Role Description

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.

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.

Qualifications

• 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.

Requirements

• 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, managed services, or similar infrastructure-based companies.

Skills and Core Competencies

• Ability to assess technical tradeoffs involving solution quality, cost, latency, performance, scalability, maintainability, security, privacy, and operational fit.
• Strong written and verbal communication skills, including the ability to explain complex GenAI concepts, risks, and recommendations clearly to mixed audiences.
• Strong analytical and experimental approach to model and workflow evaluation, including defining acceptance criteria and interpreting evaluation results.
• Ability to work independently and across project teams, exercise sound technical judgment, identify risks early, and recommend approaches that can be supported at scale.

Success Measures

• Delivery of reliable, supportable GenAI tools, services, or reference implementations that meet defined acceptance criteria and are adopted by project teams.
• Build and maintain clear and current documentation, standards, and operating guidance that support consistent implementation and support practices.
• Early identification of GenAI design issues, security or responsible AI risk, resource constraints, and operational gaps with actionable recommendations that improve AI platform readiness.
• Demonstrable improvements in solution quality, latency, cost, reliability, or supportability through effective evaluation, architecture, and operational practices.

Benefits

• Starting pay range for this remote role is 92K-131K, reflecting the minimum and maximum target compensation for the position across all US locations.
• This position may also be eligible for incentive compensation based on individual and/or company performance.
• Workplace type: Remote Working.