Generative AI Solutions Architect

Accountabilities:: Design, develop, test, deploy, and maintain GenAI-enabled applications, services, APIs, and reusable components for internal platforms and project teams. Evaluate foundation models and deployment strategies, including open-source and locally hosted solutions such as Qwen and Ollama-based deployments, using quality, latency, cost, security, privacy, and platform-fit criteria. Design and integrate model-driven workflows using prompting, retrieval-augmented generation (RAG), tool use, and agentic patterns, supported by appropriate testing, evaluation, guardrails, and fallback mechanisms. Provide early technical guidance to project teams on feasibility, architecture, model selection, data requirements, GPU capacity, security, responsible AI, performance, and operational risks. Develop and maintain reference designs, technical documentation, evaluation methodologies, operating procedures, standards, observability guidance, and recommended GenAI usage patterns. Partner with software engineering, infrastructure, platform, security, and operations teams to ensure solutions are practical, secure, scalable, and supportable. Educate technical and non-technical stakeholders on GenAI capabilities, limitations, risks, and recommended approaches. Provide actionable feedback to senior leadership on AI platform gaps, technology priorities, and opportunities for improvement. Develop recurring reporting on significant AI initiatives, activities, and project outcomes. Maintain current documentation and guidance covering recommended AI models, tools, platforms, and resources, adapting recommendations as technologies, standards, and organizational needs evolve. Identify design issues, security and responsible AI risks, resource constraints, and operational gaps early, providing actionable recommendations. Drive measurable improvements in solution quality, latency, cost, reliability, scalability, and supportability through effective architecture, evaluation, and operational practices. Build reliable GenAI tools, services, and reference implementations that meet defined acceptance criteria and achieve adoption by project teams. Requirements Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related field, or equivalent practical experience. Demonstrated experience designing, building, and supporting GenAI-enabled applications, services, or internal tools within an enterprise or applied engineering environment. Working knowledge of software engineering practices relevant to GenAI, 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. Demonstrated experience developing and integrating model-driven workflows, including prompting, inference patterns, testing, evaluation, and production support practices. Experience collaborating across software engineering, infrastructure, architecture, security, operations, and business stakeholders to translate use cases into practical technical solutions. Demonstrated experience creating technical documentation, implementation guidance, standards, or operating procedures for both technical and non-technical audiences. Experience working within network services, telecommunications, managed services, or similar infrastructure-based organizations. Ability to assess technical trade-offs involving solution quality, cost, latency, performance, scalability, maintainability, security, privacy, and operational fit. Strong analytical and experimental mindset, including the ability to define acceptance criteria, evaluate models and workflows, and interpret results. Strong written and verbal communication skills, with the ability to explain complex GenAI concepts, risks, and recommendations clearly to diverse audiences. Ability to work independently across multiple project teams, exercise sound technical judgment, identify risks early, and recommend approaches that can operate effectively at scale. Comfortable working collaboratively with technical leadership, project teams, and non-technical stakeholders. Ability to balance innovation with responsible AI, security, operational, and enterprise requirements. Benefits $92,000–$131,000 USD target compensation range for the remote U.S. role. Actual compensation determined based on factors including work location, relevant experience, technical skills, and qualifications. Potential eligibility for incentive compensation based on individual and/or company performance. Fully remote work arrangement within the United States. Opportunity to work hands-on with generative AI, foundation models, RAG, agentic workflows, AI APIs, and locally hosted models. Exposure to GPU infrastructure, AI evaluation, cloud/platform architecture, security, and enterprise-scale AI adoption. Cross-functional collaboration with software engineering, infrastructure, platform, security, operations, and business teams. Opportunity to establish reusable AI architecture, standards, documentation, and operating practices. Direct impact on the reliability, scalability, security, and adoption of enterprise GenAI solutions. Environment focused on technical innovation, continuous learning, and practical AI implementation.