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About This Role
About Us
At Goosehead Insurance, we’re changing the way people think about insurance through a technology\-driven, client\-first approach. By combining a powerful digital platform with an expert agent network, we empower clients to find the best coverage for their needs – with transparency, efficiency, and trust at the core.
We’re building a data\-driven culture that puts analytics at the center of our decision\-making. Our team works on high\-impact projects across underwriting, marketing, client experience, and agent performance. If you're excited to apply machine learning, statistics, and data engineering to solve meaningful business problems, we’d love to hear from you.
Who You Are:
You are an AI\-focused software engineer with a strong foundation in building production systems powered by large language models (LLMs), natural language processing (NLP), and intelligent automation. You thrive at the frontier of research and application, capable of turning cutting\-edge models into secure, reliable, and scalable software. You’re comfortable in both Fortune 100–style environments and startup\-style innovation, and you want to grow your career into senior AI leadership roles.
Key Responsibilities:
- Design, build, and deploy agentic AI systems that leverage LLMs, NLP, and retrieval\-augmented generation (RAG) pipelines to support both client\-facing and agent\-facing use cases.
- Collaborate with data scientists, product managers, and engineers to translate ambiguous business problems into robust AI\-driven solutions.
- Develop and maintain scalable APIs and microservices in Python and/or Java that integrate AI capabilities into enterprise applications.
- Implement best practices in MLOps, ensuring reproducibility, observability, and seamless deployment of AI models into production.
- Drive innovation by staying current with advancements in generative AI, multi\-agent frameworks, and reinforcement learning.
- Contribute to the culture of security, compliance, and ethical AI adoption across the organization.
- Mentor junior engineers and contribute to the growth of Goosehead’s AI talent pipeline.
Required Qualifications
- 3–9 years of professional experience in software engineering or applied AI.
- Proficiency in Python and Java, with demonstrated experience building production\-grade systems.
- Strong background in NLP, LLMs, and generative AI, including prompt engineering, fine\-tuning, and orchestration frameworks.
- Hands\-on experience with MLOps pipelines, containerization (Docker/Kubernetes), and CI/CD.
- Experience working with both structured and unstructured datasets.
- Strong problem\-solving and communication skills, with the ability to explain technical concepts to non\-technical stakeholders.
Preferred Qualifications
- Exposure to both Fortune 100 enterprises and startup environments, with the ability to balance rigor and speed.
- Experience with cloud platforms (Azure, AWS, or GCP) and vector databases.
- Familiarity with agentic AI frameworks, autonomous workflows, or multi\-agent coordination systems.
- Background in insurance, financial services, or other regulated industries.
- Experience integrating AI into mission\-critical, client\-facing applications.
Benefits Summary
- High quality voluntary health, vision, disability, life, and dental insurance programs
- 401K Matching Plan
- Employee Stock Purchase Plan
- Paid holidays, vacation, and sick leave
- Corporate sponsored programs to enhance employee physical, financial, mental, and emotional wellness
- Financial Solution Program
Equal Employment Opportunity:
Goosehead is an equal\-opportunity employer and complies with all applicable federal, state, and local laws, rules, guidelines, and regulations. Goosehead strictly prohibits and does not tolerate unlawful discrimination against employees, applicants, or any other covered person because of race, color, religion, creed, national origin, ancestry, ethnicity, sex (including pregnancy, childbirth, and related medical conditions), sexual orientation, gender, gender identity, transgender status, age, physical or mental disability, veteran status, uniformed service, genetic information, or any other characteristic protected by applicable law. All applicants for employment and all Goosehead employees are given equal consideration based solely on job\-related factors, such as qualifications, experience, performance, and availability.
To learn more about our job opportunities, apply here. We look forward to speaking with you!
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 3,708 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Goosehead Insurance, 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 $238,500 based on 58 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Goosehead Insurance AI Hiring
Goosehead Insurance has 1 open AI role right now. They're hiring across AI Agent Developer. Based in Westlake, OH, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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