Generative AI Solutions Architect

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Generative AI Solutions Architect based in the United States.

This role combines software engineering, AI architecture, and stakeholder consulting to accelerate practical and sustainable adoption of generative AI.
You will design, build, deploy, and support AI-enabled applications, services, APIs, workflows, and reusable capabilities.
The position focuses on turning emerging GenAI technologies into secure, scalable, and supportable enterprise solutions.
You will evaluate foundation models and deployment approaches while balancing quality, latency, cost, privacy, security, and operational requirements.
Working across engineering, infrastructure, platform, security, operations, and business teams, you will translate complex use cases into actionable technical solutions.
You will also establish reference architectures, evaluation practices, documentation, standards, and guidance that enable consistent AI implementation.
This is a highly collaborative, technically hands-on environment where sound judgment, experimentation, and clear communication are essential.

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 prac…