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 Description
We are seeking an AI Architect located 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 is 70% 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.
Responsibilities
AI Architecture Leadership
Own 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 Data
Design 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 Architecture
Design 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 Enablement
Define 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 Contribution
Contribute 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.
Mentor 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 client stakeholders.
Identify technical risks and capability gaps, recommending improvements to engineering leadership.
Qualifications
Technical Skills:
10+ years of professional experience in software engineering, including designing and architecting enterprise software solutions.
3+ years of experience designing and delivering production AI solutions leveraging Large Language Models.
Strong experience designing enterprise AI systems using Retrieval-Augmented Generation (RAG), agentic AI, tool calling, prompt orchestration, and multi-agent workflows.
Hands-on experience with Python and modern backend frameworks such as FastAPI or equivalent.
Experience with Azure AI services, including Azure AI Foundry, Azure AI Search, Azure OpenAI, or equivalent AI platforms.
Experience building AI applications using frameworks such as Semantic Kernel, AutoGen, Microsoft Agent Framework, LangGraph, LangChain, or similar orchestration frameworks.
Strong understanding of enterprise data integration, APIs, vector databases, semantic search, and knowledge management architectures.
Experience designing AI evaluation, monitoring, observability, and governance strategies.
Strong understanding of Azure architecture and cloud-native application design.
Experience implementing CI/CD pipelines and Infrastructure-as-Code.
Familiarity with cloud security, identity management, RBAC, and enterprise governance principles.
Soft Skills:
Strong technical leadership with the ability to influence engineering teams and executive stakeholders.
Ability to translate business objectives into scalable AI architectures.
Excellent problem-solving and architectural decision-making skills.
Strategic mindset combined with hands-on execution.
Excellent written and verbal communication skills.
Comfortable operating in client-facing environments with evolving requirements.
Ability to navigate ambiguity and establish technical direction for complex AI initiatives.
Bonus Skills:
Experience with Microsoft Fabric and Power BI semantic models.
Experience with Chainlit, Streamlit, or similar AI application frameworks.
Experience with AI evaluation tools such as Azure AI Foundry Evaluations, Promptfoo, Ragas, or DeepEval.
Familiarity with Responsible AI practices, model governance, content safety, and AI risk management.
Experience with vector databases such as Pinecone, Azure AI Search, Weaviate, or pgvector.
Azure certifications such as AZ-305, AI-102, or other AI/cloud architecture certifications.
Why Upstart13?
We put people first at Upstart 13! We believe the world is filled with amazing people and we are willing to go to great lengths to seek out others who share our values to join our cause of bringing down borders in technology for people everywhere.
We develop leaders at Upstart 13, we focus on what matters to do meaningful work, we own our shit, we stay curious, and we understand responsibility leads to giving. We do big things together!
Perks:
Are you ready to join our cause? Be sure to ask, “why 13?”
