Software Architect

Serve as full-time technical lead across the engineering org: set the agenda, drive decisions to closure, own the decision record, and follow through on adoption across teams.

Partner with Engineering leadership and the Staff/Lead engineers across teams to prioritize cross-cutting work and connect it to team roadmaps.

Mentor Staff-level engineers on system design, technical writing, and influence — raise the bar for what "Staff" means at Fingerprint.

Build on the design review practices we already have (RFCs, cross-team design reviews) and make them more consistent, lighter-weight, and more useful, so teams choose to use them.

Codify and extend standards for observability, quality, security-by-design, and API evolution/versioning, working with Cloud Platform, Security, and product teams.

Review and sign off on critical designs; teach through review rather than gatekeeping.

Translate business trajectory (Tier 1 enterprise customers, new product launches, AI/MCP workflows) into a multi-quarter technical strategy and sequence the work.

Advise Engineering leadership and executive team on build/buy, platform investment, and technical risk in plain language.

Lead AI adoption in engineering practice: set norms for AI-assisted design, coding, and review across teams, and shape our architecture and documentation so AI agents can work in our systems as effectively as engineers do.

Stay hands-on: prototype, write reference implementations and documentation, and dig into production when the problem demands it.

10+ years of software engineering experience, including 3+ years operating as an Architect for a 100+ Engineering org — you have owned architecture for a platform, not just a service.

Track record of leading through influence: you have led senior engineers you didn't manage, run design review or architecture forums that people actually used, and driven adoption of standards across an org.

Deep expertise in distributed systems and high-throughput, low-latency backend architecture (Go or similar systems language; Kubernetes/AWS; event-driven and data-intensive systems). Fluency across adjacent layers — client SDKs, data pipelines, ML serving — is a strong plus.

Experience designing public APIs and SDKs at scale, including versioning, backward compatibility, and multi-surface consistency.

Demonstrated ability to anticipate systemic risk and act on it — you can point to failures you prevented, not just ones you fixed.

Exceptional written communication. You make complex decisions legible to engineers and executives alike, and you default to async, documented decision-making.

AI-native by default. You use AI coding and reasoning tools as a normal part of how you design, prototype, review, and write — and you have opinions, from experience, about where they accelerate engineering work and where they don't yet.

Architect for an AI-assisted org. You think about how codebases, documentation, service boundaries, and APIs should be shaped so that both humans and AI agents can work in them safely — legible structure, strong contracts, automated verification.

Pragmatism over purity. You balance long-term architecture with delivery pressure and know when "good enough" is the right call.

Comfortable being the first: you have stepped into a dedicated role where the work was previously shared across a group, earned the trust of the people already doing it, and made them more effective rather than displacing them.

Experience in fraud detection, identity, device intelligence, or other adversarial domains.

Multi-region / cell-based architecture experience.

Experience with MCP, LLM tool integrations, or agent-facing API design.

Familiarity with ClickHouse, Kafka, or similar high-volume data infrastructure.