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About This Role
About Nerdio
At Nerdio, our mission is to simplify the lives of IT professionals and maximize their Microsoft cloud and end user computing investments.
We support organizations of all sizes looking to deploy, manage, and cost\-optimize native Microsoft technologies. We partner with Enterprises and Managed Service Providers all over the world to add value on top of their existing native Microsoft investments like Azure Virtual Desktop (AVD), Windows 365, and Microsoft Intune.
Created in 2016, Nerdio has always taken a market\-leading and collaborative approach to cloud deployment and management. In fact, our product roadmap is greatly influenced by the regular feedback we receive from having seen companies deploy AVD into production environments several thousand times using Nerdio technology.
Today, Nerdio is used in over 50 countries by more than 15,000 organizations of every size and vertical. We’re committed to delivering exceptional service and support, which starts with identifying and supporting the best staff possible.
We are a fast\-moving, nimble company looking for individuals who are collaborative, empathetic, driven and who love to move at the speed of light. If you want to be part of the AVD transformation that Microsoft and Nerdio are leading, then we want to speak with you.
About the role
Nerdio is looking for a GTM AI Architect to lead how our marketing organization harnesses AI, data, and automation to work smarter and faster. This is not a traditional technical role — it is a strategic practitioner role for someone who deeply understands modern AI tools and knows how to apply them to real go\-to\-market problems.
You will be embedded across the marketing team, partnering with demand generation, content, field marketing, and campaign operations to identify where AI and automation can have the greatest impact. You will then help build those solutions — using tools like Claude, Claude Code, and Claude Workspace — and ensure the rest of the team can use and scale them effectively.
You will also own the architecture of how Nerdio’s marketing team organizes and governs its AI usage: building a central repository of capabilities, skills, prompts, and reference assets that drive consistency and compounding value over time.
This role is for someone who is fluent with AI tools as a practitioner, energized by workflow problems, and excited to help a fast\-moving marketing team unlock a genuine competitive advantage through intelligent automation.
What you'll do
Use Case Identification \& Prioritization
- Work closely with team members across marketing to identify the highest\-value opportunities to improve efficacy and efficiency through AI, data, and automation.
- Evaluate and prioritize use cases based on potential impact, feasibility, and data readiness, and maintain a clear roadmap of initiatives communicated to stakeholders.
- Stay current on the evolving AI tool landscape and bring forward\-looking recommendations on where to invest next.
Data Strategy \& Sourcing
- For each priority use case, determine what data is needed to drive the best outcome, whether from the CRM, intent platforms, engagement tools, third\-party sources, internal assets, etc.
- Work cross\-functionally with RevOps, Sales, and IT to pull relevant data together in a structured and accessible way.
Automation \& Agentic Workflow Building
- Partner with marketing stakeholders to design and build automation and agentic workflows that improve the quality and speed of their work.
- Use Claude, Claude Code, Claude Workspace, and related tools to create working solutions — from prompt\-driven workflows to multi\-step agentic processes that run with minimal manual intervention.
- Iterate on deployed workflows based on real usage and outcomes, continuously improving impact.
- Act as a thought partner and co\-builder for team members who want to apply AI to their specific function, making complex capabilities accessible and practical.
AI Capability Repository \& Enablement
- Architect and maintain a central repository of AI capabilities, skills, prompts, and reference assets that the marketing team can access, reuse, and build on.
- Establish standards and lightweight governance for how the marketing org uses AI tools — ensuring alignment, consistency, and scalability as usage grows.
- Enable team members to adopt new workflows and build their own AI fluency through documentation, training, and hands\-on support.
- Ensure that knowledge and solutions developed for one function are systematically available to the rest of the team, preventing duplicate effort and compounding gains over time.
Qualifications
- Fluency with Claude, Claude Code, Claude Workspace, and related AI tools — able to build working agentic workflows, design multi\-step automation, and get practical results, not just generate outputs.
- Strong working knowledge of AI\-powered workflow tools and how to connect them to real marketing use cases (content production, campaign ops, lead management, reporting, and similar).
- Experience working in or alongside a B2B marketing team, with genuine understanding of how demand generation, content, field, and campaign functions operate.
- Ability to identify, frame, and prioritize problems clearly — moving from a fuzzy workflow complaint to a structured solution without needing heavy oversight.
- Strong communicator who can explain AI capabilities and workflow designs to non\-technical stakeholders and get buy\-in for new ways of working.
- Comfortable operating in a fast\-moving environment where the tools, best practices, and organizational needs are all evolving simultaneously.
Preferred Qualifications
- Experience building with automation platforms such as n8n, Zapier, Make, or similar tools.
- Familiarity with CRM and marketing automation platforms — HubSpot, Salesforce, or similar — and how data flows between them.
- Working knowledge of intent and enrichment platforms such as ZoomInfo, 6sense, or Bombora.
- Experience creating internal enablement resources: prompt libraries, workflow documentation, training guides, or AI playbooks.
- Background in a high\-growth B2B SaaS or technology company.
Tools \& Technology Landscape
The GTM AI Architect will work across a modern GTM and AI stack including:
AI \& Automation
Claude, Claude Code, Claude Workspace
CRM \& Marketing Automation
HubSpot, Salesforce, and internal tools
Intent \& Enrichment
ZoomInfo, 6sense, HGInsights, Cognism, Gong
Analytics \& Reporting
Google Analytics, Power BI / Tableau
Benefits and Incentives
- Competitive Base and Incentive Plan
- Stock Options
- Health and Welfare Plans\*
- Life and Disability Plans\*
- Retirement Plan\*
- Unlimited Flexible Paid Time Off, including your birthday off!
- Collaborative Team Culture
- Benefits for international employees, outside the US, vary by country.
Nerdio is committed to a diverse and inclusive workplace. Nerdio is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
The pay range for this role is:
110,000 \- 130,000 USD per year(Remote (United States))
Salary Context
This $110K-$130K range is in the lower quartile for AI Architect roles in our dataset (median: $225K across 99 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 26,159 AI roles we're tracking, AI Architect positions make up 1% of the market. At Nerdio, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $292,900 based on 108 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($120K) sits 59% below the category median. Disclosed range: $110K to $130K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Nerdio AI Hiring
Nerdio has 2 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Based in Remote, US. Compensation range: $130K - $130K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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