Software & AI Architect

About brightfin:

Brightfin is the AI-native enterprise platform helping Fortune 500 and mid-market companies optimize IT spend across IT Financial Management (ITFM), Technology Expense Management (TEM), and FinOps. We are growing aggressively, with a clear and ambitious path ahead. Our customers include large healthcare systems, financial institutions, federal government agencies, and global enterprises spanning both domestic and international markets — organizations where data security and trust are non-negotiable.

Job Summary: The Software & AI Architect reports to Brightfin’s Chief Product & AI Officer and owns the technical architecture of the platform end to end: the software design behind our products, the AI-native capability built into them, the AI-driven development lifecycle the engineering org runs on, and the technical infrastructure and guardrails that make AI safe to use company-wide. This is a builder role. The Software & AI architect writes code, ships working systems, and proves ideas before anyone hires a team to scale them.

Essential Functions / Duties / Responsibilities:

Software architecture and platform design

• Own the end-to-end technical architecture of brightfin’s product suite, including platform-native builds and any standalone SaaS products, and the decisions that keep the platform scalable, secure, and fast to extend.
• Set engineering standards for quality, scalability, and reliability that hold up for enterprise, federal, and international customers alike.

AI strategy and roadmap

• Define brightfin’s point of view on where AI creates real advantage across the platform and turn that point of view into a roadmap the Chief Product & AI Officer and executive team can act on.
• Prioritize initiatives by the outcome they produce for customers or the business, tied directly to brightfin’s growth goals — not by novelty.

AI-native product capability

• Design and build AI-native capability inside the brightfin platform, including agentic features embedded across the product suite.
• Own the technical foundation these capabilities run on: model selection, orchestration, prompt and agent architecture, and the evaluation pipelines that show whether the AI is working.

AI-driven development lifecycle (AIDLC)

• Partner with the Chief Product & AI Officer and engineering leadership to build and operate the AI-driven development lifecycle that shortens brightfin’s path from concept to shipped product.
• Build inside the codebase, not above it — the tooling and workflows engineers use every day, used first-hand by the person who built them.

AI infrastructure, security, and governance

• Own the technical backbone behind responsible AI use at brightfin: model and vendor evaluation, data handling and guardrails for AI tools, access management, and integration standards.
• Partner with People & Culture and the AI Champions network on the systems behind brightAI’s security-first principle. People & Culture owns training and adoption; this role owns the infrastructure and guardrails underneath it.

Build now, scale later

• Operate as an individual owner in year one: prototype, ship, and prove out architecture and AI initiatives directly, without waiting on a team that doesn’t exist yet.
• Make the case for the team brightfin needs — with working systems, not slide decks — once an initiative is ready to scale, then recruit and lead it.

Executive partnership and reporting

• Partner with the Chief Product & AI Officer and the executive team on architecture and AI investment decisions, tying every initiative to a measurable outcome: adoption, velocity, cost, or revenue.
• Report on initiatives with the same rigor brightfin expects of its own product: clear metrics, honest status, no spin.

Qualifications:

Experience

• 8+ years in software engineering or architecture, with direct, hands-on experience designing and shipping production systems at scale — not proofs of concept.
• 3+ years leading or contributing to teams within AI-native organizations, with deep experience across LLMs, RAG, agentic-native system design, and multi-agent architectures, and a strong understanding of how AI-first companies structure engineering and scale these capabilities
• Hands-on software architecture experience: owning and evolving the technical design of a production platform, not just reviewing someone else’s.
• Hands-on AI architecture experience: designi…