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About This Role
Jackson Family Wines is much more than a family\-owned wine company. We're risk\-taking innovators with a strong belief that quality is paramount. We're always on the lookout for top talent \- individuals who possess a strong work ethic, commitment to quality and share our beliefs in healthy lands and a healthy community. We are seeking a motivated technologist to help drive our agentic AI program.
POSITION SUMMARY:
The Senior AI Agent Developer plays a critical role in shaping how Jackson Family Wines designs, builds, and scales enterprise agentic AI capabilities. This role is responsible for building personal user agents, AI workflow agents, and application agents. The position balances hands on agent development with self\-service stewardship, ensuring AI agent solutions follow best practices and are secure, supportable, and aligned with JFW’s standards. The role operates primarily within approved enterprise AI platforms (including Microsoft Copilot and Copilot Studio) and focuses on delivering production agentic solutions. In addition, the role is encouraged to continuously explore and evaluate emerging AI tools and capabilities in the broader marketplace to drive continuous innovation, influence future standards, and advance the organizations' AI maturity. Success in this role is measured by the ability to create high impact business value, improve efficiencies, deliver repeatable processes, accelerate AI adoption, and build durable AI capabilities that scale across teams. ESSENTIAL FUNCTIONS/DUTIES:* Leads by example to design, build/enhance, integrate, and test AI agents that support real business workflows.
- Establish and maintain enterprises AI agent standards for agent behavior, data access, monitoring, auditability, escalation paths, and responsible use
- Create and support a secure AI Agent self\-service enablement model, including templates, standards, and guardrails that drive user adoption and enable teams to build agents safely.
- Define AI best practices for agent design, prompting, grounding, integration, testing, and lifecycle management and drive enterprise adoption of best practices.
- Partners with stakeholders and the IT team to align solutions with adoption goals, change impacts, and monitoring requirements, ensuring successful delivery.
- Perform project management and requirements gathering activities to drive successful agentic AI deployment and adoption.
- Develop and maintain technical documentation and runbooks to ensure agents are supportable and sustainable.
- Responsible for providing support for software, end users, and self\-service initiatives.
- Drive innovation cross functionally with customers, peers, and vendors to improve company efficiency and effectiveness.
- Build partnerships with business customers, peers and stakeholders and work closely with them to shape and influence the solution design.
- Identify deficiencies with software and/or application architecture design, determine root cause, and provide corrective action.
- Keep abreast of current software development trends and impacts to the company. Recommend changes when appropriate.
- Participate and provide after\-hours and weekend escalated support as part of a rotating schedule and as needed.
JOB REQUIREMENTS:
*Essential Skills, Experience, and Education** Bachelor's degree in computer science/engineering/business or related field, plus 5 years relevant work experience; or 9 years equivalent experience.
- Proven experience as a developer delivering enterprise grade solutions or platforms from design through production support.
- Proficiency with one or more programming languages such as : Python, TypeScript/JavaScript, SQL.
- Experience with API based integrations, service to service authentication, and cross system workflows.
- Experience working with enterprise AI chat and agent platforms, such as Microsoft Copilot.
- Experience with modern AI frameworks (OpenAI, Gemini, Anthropic, Copilot).
- Experience and participation in self\-service enablement programs.
- Ability to Lead teams and provide work and technical direction to vendors and cross functional team members
- Proven track record in the design, programming and debugging of cross application systems and multiple application integration processes.
- Solid experience of root cause analysis, debugging program code, and root cause resolution.
- Ability to lead enterprise application development projects across all aspects of the software development life cycle.
- Experience with SDLC, waterfall, and scrum/agile project methodologies.
- Excellent planning, organizational, problem solving, and analytical skills.
- Excellent communicator both written and verbal.
WAGE TRANSPARENCY
The base salary for this role ranges from $122,572\.13 – $152,131annually. Compensation will be determined by candidate experience, skills, and location.BENEFITS* Health benefits – Medical, Dental, Vision, Disability \& Life Insurance
- 401(k) with employer match
- Generous time off including vacation, holidays, and paid health time
- Paid volunteer time
- Learning \& Development opportunities
- Modern Health virtual mental health \& coaching visits
- Wine discounts!
Jackson Family Wines is an Equal Opportunity Employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, or any other characteristic protected by applicable law. All employment decisions are based on merit and business needs.
Salary Context
This $122K-$152K range is in the lower quartile for AI Agent Developer roles in our dataset (median: $192K across 41 roles with salary data).
View full AI Agent Developer salary data →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 3,823 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Jackson Family Enterprises, 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 $245,040 based on 106 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($137K) sits 44% below the category median. Disclosed range: $122K to $152K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Jackson Family Enterprises AI Hiring
Jackson Family Enterprises has 1 open AI role right now. They're hiring across AI Agent Developer. Based in Santa Rosa, CA, US. Compensation range: $152K - $152K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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