Software Architect

We're hiring a Software Architect to own the end-to-end design of a complex software product. The role spans the full architectural surface (data, services, integrations, infrastructure, security, reliability) and sits at the intersection of engineering, product, and the customer environments the product runs inside. The right person treats constraints like multi-tenancy, legacy interoperability, cost, and regulatory exposure as first-class design inputs, not afterthoughts.

This is a hands-on architect role, not a diagrams-and-handoffs role. You're equally comfortable working in the abstract (RFCs, system models, trade-off analysis) and in the concrete (debugging a memory leak, profiling a hot path, rewriting an integration that's silently drifting).

You're comfortable in ambiguity. You can join a product mid-flight, form a sharp view of what's load-bearing and what's incidental, and propose a direction the team can rally behind. You treat AI-augmented engineering as a force multiplier you wield with judgment, neither a gimmick nor a substitute for thinking.

Required Qualifications

• 10+ years building production distributed systems, with at least 3 years in a Staff, Principal, or Architect role
• Strong fluency in JavaScript/TypeScript, React, and Node.js; comfortable reading and contributing across multiple languages and stacks
• Deep AWS architecture expertise: has designed and operated non-trivial multi-tenant systems across compute (ECS/EKS/Lambda), data (RDS, DynamoDB, S3), networking (VPC, ALB, PrivateLink), and identity (IAM, KMS, Secrets Manager); able to reason about cost, blast radius, and failure modes at the service level, not just the diagram level
• Writing code on critical paths, reviewing designs and PRs, taking ownership when the parts you designed misbehave in production, and pushing back when a feature request would compromise the architecture

Preferable

• Experience with Python
• Communicating architecture clearly through C4 models (context, container, component) and lightweight ADRs that capture decisions, context, and trade-offs so the team doesn't re-litigate them six months later
• Hands-on production experience with at least one AI/LLM-powered system, including a real understanding of retrieval, prompt/context design, cost, and failure modes
• Experience integrating with at least three meaningful third-party platforms in production (e.g. Slack, GitHub, Atlassian, Google Workspace, Salesforce, Stripe, IDE extensions, MCP servers)

What this person will own and do day-to-day

Architecture and technical direction
Own the end-to-end architecture of the product: data model, services, integrations, infrastructure, security, and reliability. Communicate architecture through C4 diagrams (context, container, component, and code where it adds value) that stay current with the system. Author and curate ADRs (architectural decision records) that capture context, options, trade-offs, and consequences for every load-bearing decision. Set the architectural guardrails (boundaries, contracts, SLAs) that let the team move fast without re-litigating foundational decisions. Make build/buy/integrate decisions across platforms, frameworks, third-party services, and infrastructure. Define and evolve the canonical schema, versioning model, and core abstractions of the product.

Hands-on engineering
Write production code on critical paths, especially where the architectural risk is highest. Lead design reviews and review PRs on load-bearing parts of the system. Prototype hard problems quickly to de-risk decisions before the team commits. Profile, debug, and stabilize systems under real production conditions.

Reliability, security, and enterprise readiness
Drive the architecture for deployment, data residency, and audit. Lead the response to systemic reliability issues and convert them into durable architectural improvements. Define the access control, permissions, and audit-log model across the product. Own incident postmortems on systemic issues and translate them into roadmap-level changes.

Cross-functional and engineering collaboration
Partner with engineering leadership to translate architecture into shippable increments. Work directly with Product on what's architecturally feasible inside the current quarter vs. what's a quarter-out investment. Mentor engineers on systems thinking, trade-off analysis, and writing for design clarity. Represent the product's architecture to enterprise customers during technical evaluations and security reviews.

AI-augmented engineering practice
Use modern AI engineering tooling (IDE agents, CLI assistants, MCP servers) as a daily working layer. Set the team's working patterns for spec-driven development, evaluation gates, and AI-assisted code review. Distinguish where AI accelerates the work and where it introduces risk; encode that distinction in the delivery process.