Job description

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 right 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 — not detached from it. Job Description: Summary EnvisionWare is hiring a Principal Architect, Systems & AI 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. The right 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 — not detached from it. 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 rewrite is justified and when it isn t Multi-stack knowledge: 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 Worker Type: Regular Number of Openings Available: 1

Requirements

Must-haves

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
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 rewrite is justified and when it isn t
Multi-stack knowledge: 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

Nice-to-haves

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Keywords to mirror

aijavaangularkubernetes

Platform: workday
Effort: long

Benefits

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