# Principal Architect, AI & Developer Productivity at Togetherwork

> Location unclear · Principal · DevOps / SRE / Infra · open. Source: https://alderhq.app/jobs/greenhouse%3Atogetherwork%3A8889070002 (read 2026-11-04).

## Facts (observed)

- Company: [Togetherwork](https://alderhq.app/companies/togetherwork)
- Location text: TW Corp - Central Tech; TW Corp - Central Tech
- Where it can be done from (inferred): Location unclear (evidence: "Location: TW Corp - Central Tech; TW Corp - Central Tech")
- Posted: 2026-10-16 (18 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://togetherwork.com/application/?gh_jid=8889070002

## Pay and sponsorship

- Posted pay: not posted

## Description (the employer's text)

PRINCIPAL ARCHITECT, AI & DEVELOPER PRODUCTIVITY

Location: Austin, TX (Hybrid, 2-3 days/week onsite).

Candidates must be authorized to work in the United States for the duration of employment without requiring current or future employer sponsorship of any kind.

### About Us

TogetherWork is a $250M recurring revenue SaaS business with over 33 software applications serving 12+ vertical markets. We exist to help communities, organizations, and businesses thrive by delivering software solutions tailored to their unique needs.

With our new headquarters on South Congress in Austin, TX, we are entering an exciting phase of growth. We work hard and play hard. We love our customers, we innovate with purpose, and we won’t stop until we’ve delighted every client across the diverse ecosystem we serve.

### About This Role

TogetherWork is seeking a Principal Architect, AI & Developer Productivity, reporting to \[Reporting Manager Title\], to own how AI accelerates the software development lifecycle across the portfolio. This is a hands-on leadership role for someone who has shipped AI-augmented engineering tooling at scale and can prove measurable improvements in developer throughput, software quality, and cycle time.

You will define and operationalize the AI-assisted development stack across the organization: IDE assistants, code review automation, test generation, security scanning, documentation, and release automation. You will set the standards, build the platform, and drive adoption across product teams. You will measure outcomes against DORA metrics and retire tools that do not produce returns, regardless of how fashionable they are.

This is not a research, thought leadership, or governance-only role. We are looking for someone who has actually deployed AI tooling into production engineering organizations and can show the metrics that prove it worked.

### What You’ll Do

- Define and operationalize the AI-assisted engineering platform across the portfolio, covering IDE assistants, agentic coding tools (Claude Code, Cursor, etc.), code review automation, test generation, security scanning, documentation, and release automation.
- Architect a model- and vendor-agnostic abstraction layer so the organization is not locked into a single tool, model, or provider as the landscape evolves monthly.
- Establish reference architectures and golden paths for AI-augmented workflows that teams can adopt without forcing a single stack across all products.
- Establish acceptable use, IP protection, secret scanning, and data exfiltration controls for AI in the SDLC.
- Implement open source license scanning to prevent contamination from AI-generated code that reproduces GPL, AGPL, or other restrictive license material.
- Define audit trail and traceability standards: which AI tool wrote what code, what tests were generated, what was reviewed, and what was approved.
- Partner with Security, Legal, Compliance, and Risk to embed SOC 2, PCI, PII, SOX, data residency, and other regulatory requirements into the platform design, and support audit and risk assessment readiness.
- Embed AI-driven capabilities into CI/CD: automated pull request review, test synthesis, flaky test triage, vulnerability remediation, intelligent rollout, and incident analysis.
- Establish quality gates for AI-generated code, including coverage, mutation testing, security scanning, and license compliance before merge.
- Lead enablement across product teams: onboarding paths, paved roads, internal developer portal capabilities, and training for AI-assisted workflows.
- Treat developer experience as a product with clear roadmaps, success metrics, user research, and feedback loops.
- Distinguish real productivity from the illusion of productivity; AI tools inflate volume metrics, and commits and lines of code are unreliable in AI-native workflows.
- Report tool cost against measured outcomes, and make kill, scale, or replace decisions on tools that do not return $2 to $3 of value for every $1 of cost.
- Maintain an evaluation harness so new tools can be benchmarked against incumbents on real internal work, not vendor demos.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Atogetherwork%3A8889070002 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.