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About This Role
Requisition Number: 105969
AI GTM Specialist
Location: You will have the flexibility to work fully remote from anywhere across Arizona.
Insight at a Glance
- 14,000\+ engaged teammates globally
- $8\.2 billion in revenue in 2025
- Certified as a Great Place to work in 9 Countries in 2025
- Fortune 500 Company (No. 447\) in 2025
- Received 25\+ industry and partner awards in the past year
- $1\.4M\+ total charitable contributions in 2024 by Insight globally
Now is the time to bring your expertise to Insight. We are not just a tech company; we are a people\-first company. We believe that by unlocking the power of people and technology, we can accelerate transformation and achieve extraordinary results. As a Fortune 500 Solutions Integrator with deep expertise in cloud, data, AI, cybersecurity, and intelligent edge, we guide organizations through complex digital decisions.
Agentic Workplace Consultant
Role Overview
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The Agentic Workplace Consultant is a pivotal role within Insight’s Google Solution Line (GSL), bridging the gap between management consulting and technical execution. Leveraging our Google Solution Line’s business value expertise and the technical depth of Insight’s Agentic Factory, this consultant will work directly with Line of Business (LOB) stakeholders to move organizations from "AI\-Curious" to "AI\-Native".
Your mission is to drive Agentic Velocity by identifying high\-value business friction points and architecting autonomous, multi\-step workflows using Google Workspace and Gemini Enterprise. You will be responsible for defining the ROI of digital labor and ensuring that AI agents are not just "science projects" but production\-grade assets that solve real\-world problems.
Key Responsibilities
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1\. Workflow Discovery \& Persona Design
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- Engage with LOB leaders in HR, Finance, Sales, and Engineering to map existing manual workflows to autonomous Gemini Agent capabilities.
- Conduct "Agentic Audits" to identify "micro\-frictions" that can be solved with no\-code tools or the Gemini Agent Developer Kit (ADK).
- Develop custom user personas to understand stakeholder roles and assess the specific benefits of the Agentic Workplace platform for their daily operations.
2\. ROI \& Business Case Development
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- Lead the development of board\-ready Business Cases, including 3\-year TCO comparisons between legacy seat\-based costs and agentic productivity gains.
- Establish and report on ROI Value Metrics, such as labor hours saved and accelerated engineering output.
- Identify and define KPIs that connect AI usage directly to quantifiable business outcomes.
3\. Mutual Evaluation Plan (MEP) Execution
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- Quarterback the MEP process, a data\-driven approach designed to tailor workshops to a customer's current state.
- Lead "Day\-in\-the\-Life" sessions showcasing Gemini Enterprise features integrated into real\-world business scenarios.
- Collaborate with technical teams to ensure data readiness and security hardening for AI adoption.
4\. Strategic Advisory \& Enablement
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- Act as a trusted advisor to C\-Suite executives, maintaining relationships and overcoming account blockers to grow deals and drive adoption.
- Support the "No\-Code Academy" by training non\-technical "Citizen Developers" to build custom agents via Gemini Agent Designer.
- Establish long\-term governance models, including agent identity and access management (AIAM) protocols.
Required Expertise \& Experience
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Category
Requirement
Experience
5\+ years in management consulting or strategic sales, ideally with a focus on SaaS or AI transformation.
Product Knowledge
Deep understanding of Google Workspace, Gemini Enterprise, and the Agent Developer Kit (ADK).
Business Acumen
Mastery of TCO/ROI modeling and the ability to articulate technical benefits to C\-Level stakeholders.
Methodology
Proven experience in Organizational Change Management (OCM) and delivering structured evaluation processes like MEP.
Soft Skills
Exceptional discovery skills with the ability to "uncover the why" behind business processes.
Insight is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, sexual orientation or any other characteristic protected by law.
*Insight does not accept unsolicited resumes from recruiters or employment agencies. Unsolicited resumes will be treated as direct applications from the candidate, and recruiters or agencies who submit candidates for this position without a prior, written vendor agreement will not be eligible for any form of compensation, even if the candidate is hired.*
Posting Notes: AZ\-Home \|\| Arizona (US\-AZ) \|\| United States (US) \|\| IT Infrastructure \& Support \|\| None \|\| Remote \|\|
Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 4,317 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Insight, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $240,000 based on 96 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Insight AI Hiring
Insight has 4 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Phoenix, AZ, US, Nashville, TN, US, TN, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
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