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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal AI Systems Architect - **Company:** SAPIENCE AI CORP. - **Location:** Chicago, IL, United States - **Experience:** Expert - **Salary:** $204,000.0 - $216,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Distributed Systems, Graph Database, Azure Machine Learning, Software Systems, Systems Architecture, Large Language Models, AI Platforms, Data Management, Machine Learning Operations, Virtual Agents - **Published:** September 25, 2026 - **Apply:** https://www.juju.com/job/16_e361a756 ## About the Role * Extensive experience as a senior or principal engineer or architect on complex production systems. * Deep experience architecting AI or ML platforms end to end. * A track record of architecture that scaled with a growing product and team. * Strong systems, distributed systems, and data fundamentals. * Sound judgment on security, reliability, and trust at the architecture level. * The ability to lead through influence across many teams. * Hands-on credibility that senior engineers respect., * Experience with knowledge graphs, neuro-symbolic systems, retrieval, or agentic AI. * Experience owning inference and infrastructure at scale. * Experience in trust-sensitive or regulated domains. * A record of setting standards that lifted an organization. * Experience integrating frontier advances into production architecture. How you work * You name the real architectural problem before reaching for a design. * You hold a clear point of view and defend it with evidence. * You make trade-offs explicit and own them. * You treat trust, scale, and reliability as structural. * You lead through influence and raise the people around you. * You stay hands-on enough to be credible. Skills & Competencies * End-to-end AI platform architecture. * Cross-cutting system design and interface definition. * Scale, reliability, and trust by design. * Technical standards and patterns. * Frontier evaluation and integration. * Technical leadership through influence. * Mentorship of senior engineers. Services & Tools Experience * Distributed systems, data platforms, and ML infrastructure at an architectural level. * LLM, retrieval, vector, and graph systems. * Cloud platforms (AWS, GCP, or Azure), containers, and orchestration. * Observability, security, and reliability tooling. * Architecture and design tooling for complex systems. * Enough hands-on coding to prototype and prove out hard parts. * Deep architectural ownership of the MINERVA platform, KO graph, and COGENT architecture. Prior Experience & Background * Prior principal engineer or architect roles on large-scale AI or software systems. * Experience carrying an architecture through significant growth. * A track record of cross-team technical leadership. * Experience in AI-native systems is strongly preferred. Cross-functional partners ## Description The Principal AI Systems Architect owns that. You set cross-cutting direction, make the hard architectural calls, and keep MINERVA and COGENT coherent, scalable, and trustworthy as they grow. What you will own (Areas of Responsibility) You hold seven areas of responsibility across the platform architecture. Each one is yours to set direction on and be accountable for. 1. System architecture and coherence * Own how the KO graph, COGENT, models, serving, and product surface fit into one coherent system. * Set the cross-cutting architecture that lets teams build fast without drift. * Own the seams between subsystems, where the hardest problems live. 2. Architectural strategy and big bets * Make the architecture-level decisions that shape the platform for years. * Weigh build, buy, and bet choices with a clear point of view. * Keep the architecture aligned with where AI and the business are heading. 3. Scale, reliability, and trust by design * Design for scale, reliability, and trust as properties of the architecture, not add-ons. * Anticipate where the system will strain next and get ahead of it. * Make security and governance structural. 4. Technical standards and patterns * Set the standards and patterns that raise quality across the organization. * Reduce accidental complexity and duplicated effort across teams. * Make the right way the easy way. 5. Cross-team technical leadership * Align specialized teams around shared architecture and interfaces. * Resolve the hardest cross-team technical questions. * Multiply the impact of every team through better structure. 6. Frontier evaluation and integration * Evaluate frontier advances and decide how they fit the architecture. * Separate durable direction from passing trends. * Bring in what matters without destabilizing the system. 7. Mentorship and technical culture * Raise the technical judgment of senior engineers across the organization. * Model rigor, clarity, and honesty about trade-offs. * Help the whole organization make better architectural decisions. AI-augmented ways of working You set the architecture for an AI platform and use AI to reason about it, to explore designs, stress-test trade-offs, and move faster, while holding the judgment that architecture at this level demands. The standard is human in partnership: AI accelerates the work, you own the judgment, the interpretation, and the call. The people who create the most value here are not the ones producing the most output. They are the ones turning evidence into clear, durable decisions. What this role is not To keep the boundary clear: * This is not a people-management role. You lead through architecture and influence, not through direct reports. * This is not a single-subsystem role. You own coherence across the whole platform, not one area. * This is not an ivory-tower role. You stay hands-on enough to be credible and to prove out the hard parts. * This is not a research role. You are accountable for a production platform's architecture, not open-ended exploration., You work across the entire engineering organization, including Neuro-Symbolic AI, Applied AI, ML Infrastructure, Data and Knowledge, and Security, and you partner with the CTO on technical strategy. You own the coherence of the MINERVA platform and the COGENT architecture. How we hire We review every application, and we encourage you to apply even if you do not match every line above. Research shows that talented people, especially those from underrepresented communities, often hold back when they do not meet every qualification. If that is the only thing holding you back, apply anyway. Sapience AI is an equal opportunity employer. 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