Principal Architect - CPQ product will own the end-to-end architecture of an AI-native platform built on frontier AI models and agentic workflows. Define the technical vision across product architecture, data platforms, pricing engines, knowledge systems, and AI services while making critical build-vs-buy, platform, and model strategy decisions. Partner closely with product, engineering, and business stakeholders to create a scalable, enterprise-grade solution that transforms complex sales and pricing processes through automation and intelligence. This is a hands-on leadership role requiring deep technical expertise, architectural rigor, and the ability to attract and mentor high-caliber engineering talent.
What will you own?
• Define the architecture for a hyperscaler and LLM-agnostic product built to run on customer infrastructure - not a platform-locked implementation.
• Design the semantic data core: graph-based modeling with typed relationships, multi-tenant isolation, and provenance.
• Architect agentic services - tool-using LLM agents, orchestration, guardrails, and retrieval grounding - as first-class product capabilities.
• Set standards for async API design, state machines, code generation/templating, and draft-to-active data lifecycles.
• Lead technical delivery end-to-end, mentor engineers, and make the build-vs-buy and model-selection calls.
Must-Have Experience:
• 15+ years building software, with a proven record of shipping real products 0-to-1 through scale - not just running projects.
• Core LLM engineering: designing agentic systems with frontier models (Claude / GPT) - tool / function calling, agent skills, multi-step orchestration, and memory.
• Production RAG: embeddings, vector search, chunking and re-ranking, semantic field-mapping, and grounding model output over enterprise data.
• Prompt & context engineering: structured / JSON output, schema validation, evals and regression harnesses, guardrails, and cost / latency / quality trade-offs.
• Strong in Python and modern JS/TS (React / Vite); async services, graph / semantic data modeling, containers, and cloud (Azure / AWS / GCP).
• Core LLM engineering: designing agentic systems with frontier models (Claude / GPT) - tool / function calling, agent skills, multi-step orchestration, and memory.
• Production RAG: embeddings, vector search, chunking and re-ranking, semantic field-mapping, and grounding model output over enterprise data.
• Prompt & context engineering: structured / JSON output, schema validation, evals and regression harnesses, guardrails, and cost / latency / quality trade-offs.
• Strong in Python and modern JS/TS (React / Vite); async services, graph / semantic data modeling, containers, and cloud (Azure / AWS / GCP).
Nice to Have:
• CPQ / quote-to-cash or enterprise SaaS exposure. Salesforce or Revenue Cloud familiarity is a plus, not a requirement - this is a non-Salesforce-native product.
Candidates should demonstrate the below listed critical leadership qualities:
• Customer Advocacy - Ensures customer success on stated and unstated business priorities through strategic advice and market leading solutions.
• Owner’s Mindset - Is passionate about Brillio’s business, deeply committed to its success and makes decisions that are in the best interest of the firm.
• Innovation Ethos - Embraces ambiguity and adopts relentless experimentation to challenge current practices and enable continuous growth.
• Execution with Pace - Displays agility to deliver high quality results in the face of changing stakeholder expectations.
• Big Picture Thinking - Creates a common definition of shared success and sets bold targets that inspire the team towards new horizons.
• Winning Through Teams - Enables a culture of collaboration and empowers teams to "raise their game” to create a winning formula.
• Owner’s Mindset - Is passionate about Brillio’s business, deeply committed to its success and makes decisions that are in the best interest of the firm.
• Innovation Ethos - Embraces ambiguity and adopts relentless experimentation to challenge current practices and enable continuous growth.
• Execution with Pace - Displays agility to deliver high quality results in the face of changing stakeholder expectations.
• Big Picture Thinking - Creates a common definition of shared success and sets bold targets that inspire the team towards new horizons.
• Winning Through Teams - Enables a culture of collaboration and empowers teams to "raise their game” to create a winning formula.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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