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About This Role
Principal Architect, AI \& Developer Productivity
Location: Atlanta, GA (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
- 100% employer\-paid Basic Life and AD\&D insurance
- 401(k) retirement plan with a 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
- 12 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
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Togetherwork, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Togetherwork AI Hiring
Togetherwork has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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