AI Solutions Architect

Calling All Upstarters!AI SOLUTIONS ARCHITECT WANTED!We are Upstart 13. We are humble, hungry, and competent people who are radically changing the expectations and experience of outsourcing for all participants by challenging barriers that create inequality and by bringing down borders in technology for people everywhere. We’re all about delivering value and doing big things. We have become a game changer for teams around the world who look to Upstart’s services as a differentiator.Job DescriptionWe are seeking anAI Architectlocated in Latin America to lead the technical vision and architecture for a strategic enterprise AI engagement built on the Microsoft AI ecosystem. This is a senior, account-dedicated role responsible for defining and evolving the AI architecture that powers intelligent enterprise solutions leveraging large language models, agentic AI, enterprise data, and Microsoft Azure services. This engagement is scaling from a single pilot team toward a multi-team, multi-tenant platform, requiring an architect who can design AI solutions that are secure, scalable, observable, and reusable across the organization. The role is70% architectural leadership and 30% hands-on technical contribution. You will partner closely with the Delivery Lead, engineering teams, and client stakeholders to define AI architecture, guide technical decisions, and ensure successful integration between AI capabilities and the client’s enterprise data ecosystem. Although this role has no formal people management responsibilities, it requires strong technical leadership and influence across both internal engineering teams and senior client stakeholders. As the primary AI architecture authority for the engagement, you will be responsible for maintaining architectural standards, decision records, and implementation guidance that enable long-term scalability while minimizing operational risk and knowledge silos.ResponsibilitiesAI Architecture LeadershipOwn the end-to-end AI architecture across LLM orchestration, retrieval, enterprise data integration, and deployment.Define architectural standards, best practices, and reusable AI patterns across the platform.Evaluate architectural trade-offs involving latency, cost, model quality, scalability, security, and maintainability.Define the long-term AI strategy in collaboration with the Delivery Lead and engineering leadership.Evaluate emerging AI technologies, frameworks, and patterns, introducing them where they provide measurable business value.Design reusable AI capabilities that support multi-team adoption and future productization.Maintain architectural decision records, technical standards, and implementation documentation.Agentic AI & Enterprise DataDesign intelligent agent architectures, including tool calling, retrieval, planning, multi-step reasoning, and state management.Architect Retrieval-Augmented Generation (RAG) solutions leveraging enterprise knowledge and structured data sources.Design AI integrations with enterprise data platforms, semantic models, APIs, and knowledge repositories.Define AI evaluation strategies covering quality, hallucination detection, grounding, latency, and cost optimization.Design observability and monitoring capabilities for AI applications, including prompt performance, model behavior, and operational telemetry.Architect scalable knowledge ingestion, enrichment, and indexing pipelines to continuously improve AI accuracy.AI Platform & Cloud ArchitectureDesign scalable Azure-based AI solutions that leverage Azure AI services and modern cloud-native architectures.Collaborate with cloud infrastructure and security teams to ensure secure, resilient, and compliant deployments.Partner with security stakeholders on Responsible AI, privacy, accessibility, and governance reviews.Define deployment strategies across development, testing, and production environments.Guide identity and authentication strategies required for AI applications integrating with enterprise systems.AI Engineering & Delivery EnablementDefine best practices for AI application lifecycle management.Drive adoption of Infrastructure-as-Code, CI/CD, automated testing, and AI evaluation pipelines.Improve operational excellence through observability, monitoring, and deployment automation.Establish reusable templates and architectural accelerators for future AI initiatives.Hands-On Technical ContributionContribute to the design and implementation of AI orchestration layers and retrieval pipelines.Review architecture and code for AI services, APIs, and integrations.Support troubleshooting of complex production issues involving AI applications.Contribute directly to proof-of-concepts and critical implementation efforts when needed.Technical LeadershipMentor engineers on AI architecture, LLM best practices, and enterprise AI design patterns.Provide architectural guidance across engineering teams without formal authority.Present architectural recommendations and technical roadmaps to senior