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About This Role
Onsite
Travel: 20%
Join the Maverick Power Team!
At Maverick Power, we don’t just build power distribution solutions—we redefine industry standards. Recognized on the 2025 Inc. 5000 list as \#16 in the U.S., \#2 in Manufacturing, and \#1 in Texas, we are committed to driving innovation, quality, and speed. With multiple manufacturing facilities across North Texas and Phoenix, we are expanding rapidly and looking for top talent to grow with us
If you are ready to be part of a high\-energy, solutions\-driven team where your work makes an impact, Maverick Power is the place for you.
What We Offer:
- Competitive Salary \+ Bonus Potential!
- Paid Time Off, 401K Matching, Medical, Dental, and Vision Benefits!
- High\-growth organization with advancement opportunities!
- Diverse, Collaborative, \& Fun Work Environment!
About the Role:
Maverick Power is standing up a dedicated AI build team to make Quote\-to\-Cash dramatically faster—cutting quote cycle time from 3–5 days to under 2 hours. As a GenAI Chat Developer, you'll build the conversational interface at the heart of this effort: a chat\-based application that simplifies quoting for inside sales today, and eventually our customers. You'll own the UX, prompt orchestration, and application shell that becomes the reusable foundation for every AI use case Maverick builds next. This isn't a one\-off app—it's the platform every future "skill" gets added to.
Key Responsibilities:
- Design and build the conversational (chat) interface for the Quote\-to\-Cash application, using Python and GenAI application frameworks (e.g., Streamlit or equivalent).
- Own prompt orchestration, session management, and the overall application shell architecture.
- Collaborate with MCP/Integration Developers to consume the Quote Tool, HubSpot, and Epicor MCP servers within the chat experience.
- Build and maintain a versioned prompt and evaluation library to support consistent, high\-quality AI output.
- Iterate on the application based on inside\-sales feedback to reduce quote\-cycle time and improve the quoting experience.
- Support the transition from an internal co\-pilot MVP to a broader, and eventually customer\-facing, application.
- Contribute to the reference\-architecture mandate: build the app shell so the next AI use case is a configuration exercise, not a rebuild.
- Perform other job\-related duties as assigned.
Qualifications:
- Minimum of 2 years of professional experience in Python application development.
- Hands\-on experience building generative\-AI chat or conversational interfaces (Streamlit, Gradio, or comparable frameworks).
- Practical experience with prompt engineering and LLM application design.
- Comfortable working directly with end users (inside sales, operations) to translate real workflows into application features.
- Strong collaboration skills; able to work in a small, senior, tightly integrated team.
- Comfortable using AI coding tools (e.g., Cursor, GitHub Copilot, Claude Code, Codex) as part of the daily development workflow.
Preferred Qualifications
- Experience building or contributing to a reusable application shell or internal platform, rather than a single\-purpose app.
- Familiarity with MCP (Model Context Protocol) or similar agent\-integration patterns.
- Experience with evaluation harnesses or systematic prompt testing.
- Exposure to CRM (HubSpot) or ERP (Epicor) data and workflows.
Physical Requirements:
- Ability to sit or stand at a workstation for extended periods.
- Ability to occasionally lift up to 15 lbs (equipment, monitors, etc.).
- Regular use of computer, keyboard, and monitor for extended periods.
Work Environment:
May work in various settings at the Maverick Power facilities, in offices, in multiple shops, and in commercial buildings. Maintaining the same position or posture while performing tasks and sitting for prolonged periods. The above\-noted job description is not intended to describe, in detail, the multitude of tasks that may be assigned but rather to give the applicant a general sense of the responsibilities and expectations of this position. As the nature of business demands change so, too, may the essential functions of the position.
EEO/AAP Statement
We acknowledge and honor the fundamental value and dignity of all individuals. Maverick Power is an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
The above is intended to describe the general content of and requirements for the performance of this job. It is not to be construed as an exhaustive statement of duties, responsibilities, or physical requirements. Nothing in this job description restricts management’s right to assign or reassign duties and responsibilities to this job at any time. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
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 Maverick Power, 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.
Maverick Power AI Hiring
Maverick Power has 1 open AI role right now. They're hiring across AI Agent Developer. Based in Plano, TX, 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.
Frequently Asked Questions
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