AI Architect

Cyclotron, Inc.•Chicago, IL

1d•$140,000 - $180,000•Remote

About The Position

Cyclotron's AI Engineering team is hiring an AI Architect to help design and deliver production AI systems for our enterprise clients. This is a hands-on, client-facing architect role. You will own the architecture on your engagements, contribute to delivery leadership, and grow into the full executive conversation alongside senior architects. Our practice is anchored in the Microsoft stack and actively extending across the OpenAI and Anthropic platforms, so we expect you to be fluent across all three and pragmatic about which one is right for a given problem. More than the usual architect role, we are looking for someone visibly, obviously engaged with this space: you have been building real agentic solutions, you ship with AI coding tools, and you have informed opinions on how to architect agentic solutions. You will work directly with senior architects on real engagements, with explicit room to grow the consulting craft (scoping, executive communication, escalation) alongside the technical depth. We invest in your certifications and time for hands-on exploration of new tools and patterns.

Requirements

4+ years of enterprise software, data, or AI architecture experience, including at least 1 year architecting or shipping generative AI or agentic systems (production or substantive prototypes).
Demonstrated ownership of meaningful technical work as a technical lead, senior engineer, or architect on at least one multi-stakeholder initiative. This can come from either track and we weight them equally: consulting or professional services (scoping inputs, delivery team coordination, client communication), or an industry role leading technical implementation on projects with business stakeholders and cross-functional delivery teams.
Strong stakeholder communication, written and verbal, to technical and management audiences, whether external clients or internal business partners. You can facilitate a whiteboard session, present a design review to a skeptical engineering manager, and write a clean architecture document in the same week.
Hands-on proficiency with the Azure AI stack: Azure OpenAI, Azure AI Foundry / AI Studio, AI Search, Document Intelligence, and at least one orchestration framework (Semantic Kernel, LangGraph, or equivalent).
Hands-on proficiency with the OpenAI platform: Responses API, Agents SDK, function calling, structured outputs, and evals. You have shipped something on it, not just read the docs.
Hands-on proficiency with the Anthropic platform: Claude API, tool use, Claude Agent SDK, MCP, and skills, again demonstrated through work you can walk us through.
Microsoft Certified, Azure AI Engineer Associate (or willingness to earn within 90 days of hire). Willingness to pursue Azure Solutions Architect Expert.
Strong software engineering fundamentals. You read, write, and review code as a regular part of your work.
Able to work remotely within the United States with reliable overlap for Eastern and Central business hours.
AI-forward bar: we are looking for someone with a clear, visible history of digging into this space and building real things. You don’t need to check every box below, but most should feel like a "yes," and you should be ready to walk us through the relevant ones in a portfolio review:
A visible track record of building agentic solutions: production work, substantive prototypes, or a serious side project. Bring repos, architecture walkthroughs, recorded demos, or case studies. Ideally across more than one model family.
AI coding tools are a core part of your work (Claude Code, Cursor, GitHub Copilot, Codex, or equivalent). Bring receipts: agents you have configured, skills or prompts or slash commands you have authored, and commit history that reflects how you actually work with them.
You have built agentic systems with real tool use (MCP servers, skills, sub-agents, multi-step planning and reflection loops, or comparable patterns on OpenAI Agents SDK, Claude Agent SDK, Semantic Kernel, or similar) and can walk us through the architecture and the failure modes you hit.
Informed, curious perspective on the current state of agent orchestration: context engineering; tool-use patterns; evaluation strategy; guardrails; and cost/latency trade-offs.
Genuine passion and engagement with the work, obvious from the first conversation. You read the research, you try the tools the week they drop, you argue with your peers about where this is going.

Nice To Haves

Experience with Microsoft Fabric, Power Platform, and Copilot Studio at enterprise scale.
Experience with eval frameworks (Promptfoo, Braintrust, DeepEval, LangSmith, OpenAI Evals, Anthropic’s eval tooling) or well-designed in-house approaches.
Responsible AI and governance experience in regulated industries (healthcare, financial services, or public sector).
Depth in one or more of: RAG, fine-tuning, vector search tuning, multi-modal systems.
Consulting or professional-services experience, or prior work in a partner/ISV ecosystem.

Responsibilities

Own the architecture on AI engagements: discovery, design, delivery oversight, and knowledge transfer. You are the architect on your projects, working alongside a delivery lead and engineering team.
Serve as a credible technical voice with client stakeholders, including directors, managers, and senior engineers. Run workshops and design reviews with calm and clarity.
Architect agentic systems, RAG pipelines, and AI-augmented workflows across the Microsoft, OpenAI, and Anthropic ecosystems (Azure OpenAI, Azure AI Foundry, AI Search, Copilot Studio, the OpenAI platform including Responses API and Agents SDK, and the Anthropic platform including Claude API, Claude Agent SDK, MCP, and skills), choosing and composing them based on client fit, not vendor loyalty.
Contribute to the reference architectures, skills, prompts, and MCP servers the team reuses across clients.
Help build out our growing OpenAI and Anthropic practices alongside our Microsoft work through enablement content, reference implementations, and demos.
Pair with senior architects and engineers on the hard parts. Review designs, debug production issues, sharpen evals and guardrails.
Translate between business goals and engineering reality. Write clearly, present crisply, disagree well.
Contribute to internal IP: opinionated docs, short demo videos, and enablement sessions that upskill the rest of the team on what is actually working.