Principal Architect, Systems & AI

Job Summary

EnvisionWare is hiring an Architect to serve as the technical truth layer for a company‑wide AI‑first transformation. You will own architectural integrity across a portfolio of 40+ products, use AI to map and interrogate the existing codebase, and validate every AI‑generated technical claim before it becomes a decision. This role is the guardrail that makes speed safe. You report into the Director of Engineering and act as architecture authority across the internal team and Contour pod. Contract‑to‑hire is acceptable if that is the fastest path to the right person. The ideal candidate has deep systems‑thinking capability, practical skepticism about both legacy systems and AI‑generated output, and a track record of working close to delivery rather than as a detached enterprise architect.

What You’ll Own

• Architectural integrity across all products and major technical changes
• The architecture approval gate: every design touching system boundaries, auth, payment, or data models requires your sign‑off
• System mapping, dependency analysis, and risk discovery – accelerated by AI but validated by you
• The modernization roadmap: deciding where to stabilize, modularize, retire, or selectively rewrite
• Technical guardrails that make AI‑assisted development safe at scale

How AI Changes This Role

AI is your force multiplier, not your source of truth. You will use AI to crawl and map unfamiliar codebases, generate dependency views, surface hidden risks, and draft architecture proposals. But every AI‑generated technical finding is treated as a hypothesis until you validate it. You set the rules for what AI‑generated work requires deeper human review and you enforce them.

What We’re Looking For

• 10+ years of architecture experience in messy, real‑world production environments
• Legacy modernization expertise – strangler patterns, characterization tests, and the judgment to know when a rewrite is justified and when it isn’t
• Multi‑stack knowledge of Java, Angular, .NET, PostgreSQL, Kubernetes (or comparable breadth)
• Ability to understand unfamiliar code quickly and challenge weak assumptions – whether from humans or AI
• Practical skepticism: you use AI aggressively but trust it only after validation
• Comfort working close to delivery, code, and teams rather than operating as a detached enterprise architect

What Success Looks Like (First 90 Days)

• Run an AI‑assisted codebase crawl and produce validated system maps and dependency views
• Classify the portfolio: stabilize, modularize, modernize, retire, or selectively rewrite
• Define architecture approval rules for cross‑boundary changes
• Validate any AI‑generated technical findings before they appear in leadership or roadmap decisions
• Set the technical guardrails that make AI maturity safe and achievable