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About This Role
Say hello to Hagerty
Hagerty is a company built by drivers for drivers. We put our members at the center of everything we do and are dedicated to making it easier and more enjoyable for enthusiasts to drive and celebrate the machines they love. We’re proud to be the world’s largest insurer of collectible and enthusiast vehicles and are home to the Hagerty Drivers Club, the world’s largest car club. Our Marketplace business presents live and digital sales across the U.S. and Europe, we host a number of driving events and concours, and our award\-winning automotive journalists produce the most popular car magazine globally, alongside internationally awarded videos. We’re committed to Never Stop Driving. Ready to get in the driver’s seat? Join us!
As an AI Agent Developer, you will design, build, and continuously improve AI agents that handle real member interactions across the full customer lifecycle: from quoting and binding coverage, to policy service, to first notice of loss and claims. You will own the end\-to\-end agent development lifecycle on the Sierra platform, scoping agent behavior, designing conversation flows, integrating with Hagerty's systems, evaluating quality, and iterating relentlessly in production until each agent is the best version of itself. This is a senior individual contributor role. You are expected to operate with autonomy, partner directly with business and technology stakeholders, bring engineering discipline to everything you ship, and drive outcomes that are measurable in member satisfaction, resolution rates, and reduced manual effort.
What you’ll do
Own the Agent Development Lifecycle
- Take agents from initial scope through conversation design, tooling, evaluation, launch, and continuous iteration in production.
- Translate business goals and member needs into agent behaviors that handle complex, high\-volume interactions across quoting, policy service, and claims.
- Define evaluation criteria for each agent, then use those metrics to drive improvement in resolution rates and member satisfaction.
- Coordinate across technical and non\-technical stakeholders throughout the full build\-and\-launch process.
Design and Build High\-Quality Agents
- Design and build conversational AI agents on the Sierra platform — expressing goals, procedural knowledge, and deterministic guardrails for regulated or high\-stakes interactions.
- Develop and refine conversational flows, rewrite prompts, and run simulations to tune agent behavior against real member scenarios — edge cases, tone, brand voice, and accuracy all matter.
- Integrate agents with Hagerty's internal systems and APIs (policy administration, quoting engines, claims platforms) using appropriate authentication patterns and service accounts.
- Build and maintain immutable agent release snapshots — including model version dependencies, knowledge bases, and prompts — following established release and engineering standards.
- Apply engineering discipline in a no\-code/low\-code and declarative platforms: practical logging, error handling, monitoring, automated testing, and regression coverage for critical agent workflows.
- Validate data accuracy in agent outputs, build reconciliation and anomaly checks where outcomes touch financial or policy data, and raise issues early.
Drive Continuous Quality and Improvement
- Establish and run structured quality assurance processes — regularly auditing live conversations, annotating agent behavior, and converting findings into regression tests.
- Use annotated conversations to build a growing test suite that prevents regressions across model upgrades, prompt changes, and platform updates.
- Monitor agent performance in production, respond to incidents, and iterate rapidly based on usage signals, member feedback, and business outcomes.
- Participate in on\-call production support, sharing responsibility for incident response and agent reliability.
Shape How Hagerty Builds with AI
- Act as a trusted internal advisor on AI agent strategy — contributing data\-driven insights on what's working, where agents can expand, and how to push the platform further.
- Identify and champion new agent use cases across the member lifecycle as the technology and Hagerty's capabilities evolve.
- Contribute to team engineering standards, reusable integration patterns, and secure\-by\-default guardrails for agent development.
This Might Describe You
AI Agent \& Technical Experience
- Hands\-on experience building or deploying AI/LLM agents in production — including prompt engineering, eval frameworks, RAG, and agent tooling.
- Strong technical aptitude: comfortable reading API documentation, understanding data models and system architecture, and engaging in engineering tradeoff discussions.
- Experience with low\-code/no\-code or declarative platforms and the ability to ramp quickly on new tooling.
- Proficiency consuming and integrating APIs, including authentication patterns and working with service accounts.
Engineering Discipline
- A degree in Computer Science, Engineering, Mathematics, a related technical field, or equivalent practical experience or comparable experience at the intersection of technology and business.
- Experience applying engineering\-quality practices to no\-code/low\-code development: automated testing where possible, regression coverage for workflows, monitoring and alerting for critical paths.
- Experience with CI/CD, release management, and deployment promotion practices (dev/test/prod), including versioning and rollback awareness.
- Strong security posture: least\-privilege access, secrets management, auditability, and compliant handling of sensitive member data.
- Ability to design solutions with clear inputs, outputs, and predictable behavior — built to be maintained and observed over time.
Delivery \& Stakeholder Leadership
- Proven ability to own complex, high\-visibility projects end\-to\-end — from scoping and stakeholder alignment through production launch and continuous improvement.
- Excellent verbal and written communication skills; able to explain technical concepts clearly to non\-technical partners and drive alignment across teams.
- Strong analytical and problem\-solving skills — able to identify risks, develop mitigation strategies, and handle unexpected challenges autonomously.
- Experience working at the intersection of technology and business operations, with a track record of delivering measurable outcomes.
Nice to Have
- Experience in insurance, financial services, or another regulated industry.
- Familiarity with telephony platforms and voice agent development.
- Background in conversational design, customer experience, or contact center operations.
Other things to note
- This position is open to U.S. remote work. However, team members who reside within 20 miles of the Traverse City headquarters will follow a hybrid schedule, working from the office three days per week.
- May require travel for quarterly events.
- Familiarity with public company requirements, including Sarbanes Oxley and key regulations, if applicable. For SOX compliant roles, responsible for designing, executing, and documenting internal controls where they have been identified as owners to prevent errors in financial reporting, processes, and business operations. Including attestation to the completeness, accuracy, and compliance of all financial reporting data, where applicable.
If you reside in the following jurisdictions: Illinois, Colorado, California, District of Columbia, Hawaii, Maryland, Minnesota, Nevada, New York, or Jersey City, New Jersey, Cincinnati or Toledo, Ohio, Rhode Island, Washington, British Columbia, Canada please email [email protected] for compensation, comprehensive benefits and the perks that set us apart.
At Hagerty, we share the road. We are an inclusive automotive community where all are welcomed, valued and belong regardless of race, gender, age, or car preference. We are united by our shared passion for driving, our commitment to preserve car culture for future generations and our desire to make a positive impact in the world.
\#LI\-Remote
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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 Hagerty, 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.
Hagerty AI Hiring
Hagerty has 1 open AI role right now. They're hiring across AI Agent Developer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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