Generative AI Solutions Architect

Description

You will design, build and support AI-enabled applications, workflows and reusable capabilities, and help teams assess and adopt AI.

The role combines software engineering, AI architecture and consulting to make AI adoption secure, scalable and sustainable across the GIN organization.

The company is a $30+ billion business and technology services provider serving 75% of the Fortune Global 100.

It runs AI and digital infrastructure and has experts in 70+ countries.

It invests over $3 billion a year in research and development through its parent group.

In 2021 the Global IP Network division won Best Global Wholesale Carrier (Data) at the Global Carrier Awards.

The network is Tier-1, ranks among the top networks worldwide, covers the Americas, Europe, Asia and Oceania, and runs as a single network under AS 2914.

Responsibilities

• Build, test, ship and maintain GenAI applications, services, APIs and reusable components for internal platforms and project teams.
• Choose foundation models and deployment methods, including open-source and locally hosted options such as Qwen and Ollama setups, by weighing quality, latency, cost, security, privacy and platform fit.
• Design model-driven workflows such as prompting, retrieval-augmented generation, tool use and agentic patterns, with testing, evaluation, guardrails and fallback behavior.
• Advise project teams early on feasibility, architecture, model choice, data needs, GPU capacity, security, responsible AI, performance and operational risk.
• Write and keep current reference designs, technical documentation, evaluation methods, operating procedures, standards, observability guidance and usage patterns for GenAI.
• Work with software, infrastructure, platform, security and operations teams, teach technical and non-technical staff what GenAI can and cannot do, and give the Executive Head, SAS feedback on platform gaps and priorities.
• Produce regular reports on significant GIN AI initiatives and outcomes, and keep GIN AI guidance current, including recommended models, core tools, platforms and resources, as needs and standards change.

Requirements

• Bachelor's degree in Computer Science, Software Engineering, Information Systems or a related field, or equivalent practical experience.
• Working knowledge of software engineering for GenAI: Python or a similar language, APIs, source control, testing, containers and automated deployment.
• Working knowledge of GPU compute and memory limits, foundation model strengths and weaknesses, and open-source or locally hosted platforms such as Ollama and Qwen.
• Proven experience building, running and supporting GenAI applications, services or internal tools in an enterprise or applied engineering setting.
• Proven experience working with software, infrastructure, architecture, security, operations and business teams to turn use cases into workable solutions.
• Proven experience building and integrating model-driven workflows, covering prompting, inference, testing, evaluation and support.
• Proven experience writing technical documentation, implementation guidance or standards for technical and non-technical readers.
• Proven experience in network services, telecommunications, managed services or similar infrastructure-based companies.
• Ability to weigh trade-offs among solution quality, cost, latency, performance, scalability, maintainability, security, privacy and operational fit.
• Clear written and spoken communication, able to explain complex GenAI concepts, risks and recommendations to mixed audiences.
• Analytical, experiment-driven approach to evaluating models and workflows, including defining acceptance criteria and reading results.
• Able to work alone and with project teams, use sound technical judgment, spot risks early and recommend approaches that can scale.
• Success looks like reliable, supportable GenAI tools, services or reference implementations that meet acceptance criteria and get adopted by project teams.
• Success also means clear, current documentation, standards and operating guidance that support consistent implementation.
• Success includes flagging GenAI design issues, security or responsible AI risks, resource limits and operational gaps early, with practical recommendations.
• Success is measured by demonstrable gains in solution quality, latency, cost, reliability or supportability.

Conditions

Reports directly to the Executive Head, SAS.

Works closely with software engineering, infrastructure, platform, security and operations teams.

Engages technical leadership, project teams and non-technical stakeholders as projects require.

Remote role in the US with a starting pay range of 92K-131K, reflecting the minimum and maximum target pay across all US locations.

Actual pay depends on work location, relevant experience, technical skills and other qualifications.

The company welcomes diversity and inclusion.

Benefits

• May be eligible for incentive compensation based on individual and/or company performance.