Applied AI Architect, Partner

• Serve as the

for a portfolio of strategic partners, building trusted relationships with their executives, AI leaders, architects, engineering teams, and delivery organizations.

• Partner closely with the Revenue Partnerships team as one virtual team, aligning partner strategy, technical priorities, joint customer opportunities, and measures of success.

• Develop the

, identifying priority solutions, use cases, technical capabilities, enablement needs, delivery risks, and opportunities to drive customer adoption.

• Assess prospective and existing partners' technical capabilities and delivery readiness, identifying strengths, gaps, and the investments required to successfully deliver OpenAI-powered solutions.

• Lead technical discovery with partners and joint customers to identify, qualify, and prioritize AI use cases tied to meaningful business outcomes.

• Develop clear

spanning models, applications, enterprise data, integrations, identity, security, privacy, governance, evaluation, reliability, and deployment.

• Collaborate with partner sales and technical teams on strategic opportunities, providing technical guidance through discovery, demonstrations, workshops, prototypes, architectural recommendations, and proofs of value.

• Guide partners through their initial and most strategic OpenAI deployments, helping them navigate technical tradeoffs and production dependencies while ensuring solutions meet a high bar for quality, security, reliability, and responsible deployment.

• Build partner capability and

through tailored enablement, architectural guidance, reusable solution patterns, technical workshops, and mentorship.

• Establish clear standards and mechanisms for

, identifying risks early and intervening when architecture, implementation, security, evaluation, or adoption issues threaten customer outcomes.

• Orchestrate the right OpenAI specialists across Deployment Engineering, Product, Security, implementation, enablement, and domain teams while maintaining clear ownership and handoffs between OpenAI and partner delivery teams.

• Track partner capability, joint pipeline technical progress, production adoption, usage, solution quality, and measurable customer impact to identify where additional investment or intervention is needed.

• Identify repeatable patterns across partner engagements and turn them into architectures, playbooks, reference solutions, and best practices that allow successful approaches to scale across partners and customers.

• Bring insights from partners and joint customers back to Product, Engineering, Research, and Partnerships, helping shape product strategy, technical guidance, and the broader OpenAI partner ecosystem.

• Have significant experience in senior customer- or partner-facing technical roles such as solutions architecture, partner engineering, field architecture, technical account leadership, AI deployment, or technical consulting.

• Have worked successfully with

, and understand how partner organizations sell, architect, and deliver complex enterprise technology.

• Have guided complex enterprise organizations from technical discovery and evaluation through

.

• Can build credibility with CTOs, CIOs, CISOs, partner executives, and AI leaders while communicating equally effectively with architects and hands-on engineers.

• Bring strong software and cloud architecture foundations, including APIs, distributed systems, enterprise data, integrations, identity, security, privacy, and governance.

• Understand modern AI systems, frontier LLMs, agentic applications, model evaluation, retrieval, and enterprise AI workflows.

• Can prototype when useful, explain technical tradeoffs clearly, and work confidently with APIs, SDKs, and languages such as Python or JavaScript.

• Have experience

, turning your own expertise into repeatable architectures, training, patterns, and practices that others can successfully apply.

• Understand enterprise security and compliance requirements and can guide partners and customers through the technical considerations required to move AI systems safely into production.

• Are comfortable balancing the needs of OpenAI, the partner, and the end customer, and can exercise sound judgment when those priorities do not perfectly align.

• Know when to go deep personally, when to coach a partner toward the answer, and when to engage specialist resources.

• Can prioritize across a portfolio of partners and customer opportunities, focusing technical resources on the work most likely to create durable customer value and adoption.

• Communicate clearly and can turn ambiguity into a practical architecture, executive decision, enablement plan, or path to production.

• Own problems end-to-end, learn quickly, and care deeply about helping partners and customers deploy advanced AI responsibly.