AI Agent Developer, HR

We need to output HTML fragment with sections only if real content. Use

for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We have content: description includes "We are Lennar ..." maybe that's about the role? Could be "About the role". Responsibilities section: "Your Responsibilities On The Team ..." includes bullet list. Requirements: maybe "Your Toolbox" includes qualifications, skills, etc. Benefits: "Life at Lennar" includes benefits. Pay and Schedule not present, so omit those sections. Also need to include maybe "Qualifications" separate from "Skills"? The "Your Toolbox" includes degree, experience, consultative mindset, communication, programming proficiency, SQL, AI agent concepts, cloud platforms, etc. Could split into Qualifications (education, experience) and Skills (technical). But we can keep as one section maybe "Requirements". However instructions: Use headings for those categories if content exists. We can decide to have sections: About the role, Responsibilities, Requirements (includes qualifications and skills), Benefits. No Pay or Schedule. We must not repeat job title as heading. No metadata like Job ID. Remove boilerplate: equal opportunity statements, apply instructions, social media links, etc. Also remove duplicated lines. We need to preserve all substantive information. So we need to include the description paragraphs about Lennar, the AI Agent Developer role, responsibilities bullet list, toolbox bullet list, benefits bullet list. We must format lists with

• . For prose use

  . For headings use

  . Let's extract content. About the role: The introductory paragraphs: "We are Lennar Lennar is one of the nation’s leading homebuilders, dedicated to making an impact and creating an extraordinary experience for their Homeowners, Communities, and Associates by building quality homes and providing exceptional customer service, giving back to the communities in which we work and live in, and fostering a culture of opportunity and growth for our Associates throughout their career. Lennar has been recognized as a Fortune 500® company and consistently ranked among the top homebuilders in the United States. A Career that Empowers You to Build Your Future The AI Agent Developer designs, builds, deploys, and maintains intelligent AI agents that transform how Lennar operates across our corporate functions. Embedded directly with the teams you serve, you will sit alongside Associates to learn their workflows firsthand, then translate what you see into production-grade agents that enhance productivity, streamline operations, and enable data-driven decision making. Working at the intersection of business strategy, AI engineering, and user experience, you will partner closely with product owners, engineers, and function leaders — and carry what you learn in the field back into Lennar’s enterprise AI roadmap." We can combine into a few paragraphs. Responsibilities: bullet list from "Your Responsibilities On The Team" down to "Track and report the measures that prove value, hours returned, cycle time reduced, quality improved, and bring field learnings back to the enterprise as reusable patterns and best practices." We need to turn each sentence into a list item? Probably each bullet point corresponds to each sentence starting with a verb. Let's parse: - Embed directly with corporate function teams: shadowing Associates, mapping how work actually gets done, and partnering with product owners, business analysts, and engineers to scope high-impact AI agent use cases. - Serve as an AI advisor to function leaders: presenting recommendations, framing trade-offs and risks in business terms, and building the confidence that moves teams from curiosity to committed adoption. - Design agent personas, roles, and workflows, including decision logic, triggers, escalation paths, and human-in-the-loop rules. - Build and configure AI agents using LLMs, retrieval-augmented frameworks, orchestration tools, and relevant APIs, integrating them with internal systems, data sources, and external services. - Develop robust prompt engineering strategies, tool invocations, memory and context management, and monitoring mechanisms so agents act effectively and reliably. - Deliver working prototypes and live demos early and often, using rapid feedback from business partners to shape each agent well before it reaches production. - Test, iterate, and validate agent behavior under real-world conditions through unit tests, scenario simulations, edge-case handling, and performance and safety monitoring. - Deploy agents into production environments and establish observability through logs, metrics, and usage dashboards, along with versioning, drift detection, and lifecycle management. - Maintain agents post-deployment by refining prompts, tuning models, and improving workflows, while championing adoption through Associate training, enablement materials, user guides, and change logs. - Ensure governance, compliance, security, and ethical considerations are built into agent design, including data access, audit trails, output safety, and escalation protocols. - Track and report the measures that prove value, hours returned, cycle time reduced, quality improved, and bring field learnings back to the enterprise as reusable patterns and best practices. That seems correct. Requirements/Qualifications/Skills: The "Your Toolbox" section includes bullet-like sentences separated by periods. Let's extract each distinct requirement: - Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, Business Analytics, AI, or a related technical field; advanced degree preferred. - 2–4+ years of experience in software engineering, AI/ML development, or automation, with at least one year working with LLMs and conversational AI. - Consultative mindset with genuine curiosity about how the business actually works, and the patience to sit with Associates until the real problem surfaces. - Excellent communication and presentation skills, with the ability to demo working solutions to non-technical audiences and translate complex business requirements into functional AI workflows. - Strong programming proficiency in Python and/or JavaScript/TypeScript, with demonstrated ability to build production-ready applications. - Proficient SQL skills for querying, transforming, and joining complex enterprise datasets across multiple systems. - Working understanding of AI agent concepts including LLMs, retrieval-augmented generation (RAG), prompt engineering best practices, and agent orchestration frameworks; familiarity with LangChain, LlamaIndex, function-calling patterns, Model Context Protocol (MCP), or multi-agent architectures is a plus. - Experience with cloud platforms, particularly AWS services such as Lambda, S3, and Bedrock, plus hands-on experience with Claude, Codex, GitHub, and Spotify Backstage; agentic coding tools such as Claude Code or Codex preferred. - Experience designing for production environments including scalability, reliability, monitoring, error handling, and performance optimization; MLOps practices such as model monitoring, drift detection, and A/B testing are a plus. - Understanding of responsible AI principles and hands-on experience implementing safety guardrails, with the discretion to handle sensitive Associate and business information; familiarity with enterprise systems such as Workday, ServiceNow, and Docebo preferred. We could split into Qualifications (education, experience) and Skills (technical). But we can just have one section "Requirements" containing all. However instructions list possible headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can use both Requirements and Skills if we want. But we need to avoid thin sections. Could combine education/experience under Requirements, and technical abilities under Skills. Let's do that. Requirements: education and experience (first two bullets). Skills: rest (consultative mindset, communication, programming, SQL, AI agent concepts, cloud platforms, production experience, responsible AI). That seems reasonable. Benefits: from "Life at Lennar" paragraph: list benefits: robust health insurance plans (Medical, Dental, Vision), 401(k) Retirement Plan with $1 for $1 Company Match up to 5%, Paid Parental Leave, Associate Assistance Plan, Education Assistance Program, up to $30,000 in Adoption Assistance, up to three weeks of vacation annually, generous Holiday, Sick Leave, Personal Day policies, New Hire Referral Bonus Program, significant Home Purchase Discounts, Everyone’s Included Day. Also mention visiting Lennartotalrewards.com to view suite of benefits. We should keep that as prose maybe but we can list as bullet list. We must not include boilerplate like "Lennar Associates will have access to these benefits as outlined by Lennar’s policies and applicable plan terms." That's somewhat generic but still substantive? Might be considered boilerplate. The instruction: remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The benefits description includes some generic sentences; we can keep the substantive benefit details and maybe drop the sentence about accessing via website? It's not boilerplate but maybe okay to keep. However "Visit Lennartotalrewards.com to view our suite of benefits." is like a call to action; could be considered application instruction? Not exactly. Might keep. We also need to remove duplicated equal opportunity statements at end. Now produce HTML:

  About the role

  We are Lennar, one of the nation’s leading homebuilders, dedicated to making an impact and creating an extraordinary experience for homeowners, communities, and associates by building quality homes and providing exceptional customer service, giving back to the communities in which we work and live, and fostering a culture of opportunity and growth for our associates throughout their career. Lennar has been recognized as a Fortune 500® company and consistently ranked among the top homebuilders in the United States. A career that empowers you to build your future: the AI Agent Developer designs, builds, deploys, and maintains intelligent AI agents that transform how Lennar operates across corporate functions. Embedded directly with the teams you serve, you will sit alongside associates to learn their workflows firsthand, then translate what you see into production‑grade agents that enhance productivity, streamline operations, and enable data‑driven decision making. Working at the intersection of business strategy, AI engineering, and user experience, you will partner closely with product owners, engineers, and function leaders — and carry what you learn in the field back into Lennar’s enterprise AI roadmap.

  Responsibilities

  • Embed directly with corporate function teams: shadowing associates, mapping how work actually gets done, and partnering with product owners, business analysts, and engineers to scope high‑impact AI agent use cases.
  • Serve as an AI advisor to function leaders: presenting recommendations, framing trade‑offs and risks in business terms, and building the confidence that moves teams from curiosity to committed adoption.
  • Design agent personas, roles, and workflows, including decision logic, triggers, escalation paths, and human‑in‑the‑loop rules.
  • Build and configure AI agents using LLMs, retrieval‑augmented frameworks, orchestration tools, and relevant APIs, integrating them with internal systems, data sources, and external services.
  • Develop robust prompt engineering strategies, tool invocations, memory and context management, and monitoring mechanisms so agents act effectively and reliably.
  • Deliver working prototypes and live demos early and often, using rapid feedback from business partners to shape each agent well before it reaches production.
  • Test, iterate, and validate agent behavior under real‑world conditions through unit tests, scenario simulations, edge‑case handling, and performance and safety monitoring.
  • Deploy agents into production environments and establish observability through logs, metrics, and usage dashboards, along with versioning, drift detection, and lifecycle management.
  • Maintain agents post‑deployment by refining prompts, tuning models, and improving workflows, while championing adoption through associate training, enablement materials, user guides, and change logs.
  • Ensure governance, compliance, security, and ethical considerations are built into agent design, including data access, audit trails, output safety, and escalation protocols.
  • Track and report the measures that prove value, hours returned, cycle time reduced, quality improved, and bring field learnings back to the enterprise as reusable patterns and best practices.

  Requirements

  • Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, Business Analytics, AI, or a related technical field; advanced degree preferred.