Principal Architect, AI & Developer Productivity 

Principal Architect, AI & Developer Productivity

Location: Austin, TX (hybrid)

Schedule: Full-time | Hybrid (2–3 days in office)

Work Authorization Notice:

At this time, we are unable to provide immigration sponsorship for this position. Candidates must have current, and future, unrestricted authorization to work in the country where the role is based.

Role Overview

Togetherwork is seeking a Principal Architect, AI & Developer Productivity 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.

Key Responsibilities

1. AI Augmented SDLC Strategy and Platform

• 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.

2. Standards, Guardrails, and Governance

• Establish acceptable use, IP protection, intellectual property leakage prevention, 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, 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.

• Support audit and risk assessment readiness by ensuring platform documentation, logs, and controls meet enterprise and regulatory expectations.

3. CI/CD and Pipeline Modernization

• 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.

4. Developer Experience and Adoption

• 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.

5. Measurement and ROI

• Distinguish real productivity from the illusion of productivity. AI tools inflate volume metrics without necessarily delivering value, and traditional metrics like commits and lines of code are unreliable in AI native workflows.

• Report tool cost against measured outcomes. 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.

6. Build vs Buy and Vendor Strategy

• Evaluate and select tooling across the current market: GitHub Copilot Enterprise, Cursor, Claude Code, and emerging entrants. Negotiate enterprise terms in partnership with procurement.

• Make defensible build vs buy decisions on AI components, frameworks, and pipeline integrations based on cost, security posture, switching cost, and outcomes.

• Stay current on emerging tools and models. Recommend platform evolution quarterly rather than annually. The field moves monthly.

7. Portfolio and M&A Integration

• Bring acquired engineering teams onto the standard AI augmented SDLC platform with a clear runbook for tooling rationalization.

• Evaluate acquired company SDLC tooling and provide structured recommendations on what to integrate, rationalize, or retire.

8. Cost and Capacity Management

• Own the total cost of AI in the SDLC: license consumption, token spend, infrastructure, and developer time. Implement chargeback, cost ceilings, observability, and alerting.

• Manage token spend at scale.

• Build cost models for new tool rollouts that include training, change management, and ongoing platform support, not just license fees.

9. Collaboration and Mentorship

• Partner with engineering leaders, product, security, legal, and procurement to align platform direction with business strategy.

• Mentor senior engineers and engineering managers on AI assisted development patterns and the discipline required to use them effectively.

• Communicate architecture decisions, trade offs, and platform outcomes clearly to executive stakeholders including the CTO and CFO.

Required Qualifications

• 10+ years of proven expertise in defining end-to-end solution architecture, including: Integration patterns and enterprise system architecture, developer experience, or engineering effectiveness roles as a hands on architect or senior engineer with direct shipping responsibility.

• Demonstrated production deployment of AI assisted development tooling across multiple engineering teams, with measured outcomes. We will ask for specifics.

• Deep experience with modern CI/CD platforms including GitHub Actions, GitLab CI, CircleCI, Jenkins, Buildkite, or equivalent.

• Hands on experience with at least two of: GitHub Copilot Enterprise, Cursor, Claude Code, Kiro, or comparable AI coding tools at organizational scale.

• Strong applied LLM knowledge: prompt design, context window management, RAG patterns, evaluation harnesses, model selection trade offs, cost and latency optimization.

• Experience designing controls for IP protection, open source license scanning, secret prevention, and data exfiltration in AI assisted workflows.

• Strong systems engineering background: APIs, distributed systems, observability, cloud native architecture. AWS preferred.

• Experience operating at portfolio scale across 10 or more engineering teams with multiple technology stacks.

• Proven track record influencing without authority across protective engineering cultures and driving alignment across heterogeneous teams.

• Excellent written and verbal communication skills, including production of decision quality technical documentation.

Core Competencies

• Builder, not philosopher. Has shipped, measured, iterated, and can show the receipts.

• Outcome obsessed. Will retire a tool that does not produce returns even if it is fashionable. Will defend a boring tool that works.

• Pragmatic about risk. Understands that IP protection, security, license compliance, and audit readiness are not afterthoughts in AI augmented engineering.

• Strong influencer across heterogeneous teams. Drives alignment through evidence and paved roads, not mandates.

• Comfortable in ambiguity. The AI tooling landscape moves quickly. Makes easily reversible decisions, avoids lock in, and re-evaluates decisions as needed.

• Product minded engineer. Treats the internal developer platform as a product with users, roadmaps, and adoption metrics.

• Strategic and tactical. Comfortable moving from executive briefing to code review in the same day.

Why You’ll Love Working Here

At Togetherwork, we help community-driven organizations grow and thrive — creating better experiences for the people they serve.

We are guided by values that shape how we work every day:

• Obsess over our customers

• Own it. Together.

• Move fast with purpose

The Company offers a comprehensive employee benefits program, including:

• Medical, dental, and vision insurance options

• 100% Employer paid short/long term disability

• Basic Life

• 401(k) option with 100% company match up to 4%

• Flexible paid personal/vacation time built on mutual trust and accountability

• 10 sick days annually

• 10 company paid holidays

• 6 weeks paid parental leave

• Pet Insurance

• Medical Travel Benefits

• Infertility Benefits

• Teladoc

• Employee Assistance Program

• Wellness Benefits & Engagement Platform

Inclusion and Diversity: Togetherwork is an Equal Employment Opportunity Employer. We are a company where diverse backgrounds, experiences, and viewpoints are valued. Togetherwork does not make hiring or employment decisions on the basis of race, color, religion, gender, gender identity, sex, sexual orientation, disability, veteran status, age, ethnic or national origin, or any other basis protected by all local, state or federal laws.

Interview Process & Expectations

Our interviews are designed to be interactive and conversational. Candidates are expected to respond based on their own experience and thinking during live interviews.

To ensure a fair and consistent interview experience, the use of real-time AI tools or other external assistance to generate or guide interview responses is not permitted. Interviews are intended to reflect an individual’s judgment, problem-solving approach, and communication.

CCPA Disclosure Notice: Click Here

CCPA Disclosure Notice: Click Here