AI Solutions Engineer

We need to output only HTML fragment, using

for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to include sections only if posting has real specific content. The posting includes Company Overview, Position Summary, Responsibilities, Required Skills and Abilities, Education and Experience, What Success Looks Like, EEO Statement (to remove). No explicit Benefits, Pay, Schedule. So we should not include those headings. We need to map content to appropriate headings: About the role could be Company Overview + Position Summary? Probably "About the role" includes overview and summary. Responsibilities: list of duties. Requirements: maybe Education and Experience? Or Required Skills and Abilities? The headings list includes Skills separate. We need to decide mapping. The instruction: use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have sections: About the role, Responsibilities, Required Skills and Abilities (maybe Skills), Education and Experience (maybe Qualifications). The posting includes "Required Skills and Abilities" and "Education and Experience". Also "What Success Looks Like" could be part of About the role or maybe Qualifications? But we can include as part of About the role or separate? Since not in list, we could incorporate into About the role or Qualifications. Probably better to put under About the role. We must not repeat job title. No Job ID. Remove boilerplate like EEO. We need to use

• for any list of duties/requirements/benefits. So responsibilities list should be
  • . Also skills list maybe
    • . Education and Experience maybe list. We need to preserve all substantive information, not invent or summarize away detail. So we need to include all details from the posting (except boilerplate). Must be careful not to omit any details. Let's extract content: Company Overview: Hillwood, a Perot Company, is a premier real estate investment and development company founded on a culture of integrity, respect, excellence and teamwork. The company is a full-service real estate developer, investor and advisor focused on developing opportunities for investors, partners and communities around the world. See additional details at www.hillwood.com. Position Summary: Hillwood IT is seeking a hands-on AI Solutions Engineer to join our growing in-house IT team in Dallas, TX and help build the technical foundation for Hillwood's enterprise AI capability. This role is responsible for converting approved business use cases into secure, reliable, production-grade GenAI solutions, automations, agents, and RAG-enabled applications that deliver measurable business value. The AI Solutions Engineer will partner closely with AI Business Solutions Engineers, division stakeholders, IT architecture, security, data teams, and governance groups to transform approved AI opportunities into scalable production solutions. This is not a research-only or advisory role. Success is defined by delivering AI solutions that are technically sound, supportable, and trusted by business users while establishing reusable patterns that enable AI capabilities to scale across Hillwood. The ideal candidate is motivated by the opportunity to build more than isolated tools. This individual will help create the technical foundation, standards, and solution patterns that allow AI to become a trusted, repeatable, and high-value capability across the organization. Responsibilities: (list) Technical Solution Design and Engineering: - Partner with AI business solution leads to translate approved business requirements, success metrics, and pilot criteria into technical solution designs and solutions. - Design and build production-grade GenAI applications, automations, agents, and RAG-enabled workflows for approved business use cases. - Create technical designs suitable for Architecture Review Board (ARB), security, and production readiness review. - Develop reusable solution patterns, templates, and implementation standards that can be applied across multiple use cases and divisions. - Balance speed of delivery with reliability, maintainability, and enterprise supportability. GenAI Solution Development and Delivery: - Build, iterate, and deliver production-grade GenAI solutions for approved Horizon 1 use cases. - Design and implement agentic workflows using orchestration frameworks and multi-step agents that perform real actions. - Integrate AI solutions with Hillwood’s existing technology stack, including Claude API, ChatGPT, Copilot, Azure Functions, Power Automate, JDE, Yardi, Salesforce, SharePoint, Medius, Databricks, and other enterprise platforms. - Develop APIs, Azure Functions, workflow automations, backend services, data connectors, and application components required to operationalize AI solutions. - Own technical solution quality, including prompt engineering, structured outputs, output validation, error handling, evaluation, logging, monitoring, and reliability for business-critical use. - Develop AI coding assistant support and CI/CD pipeline integrations for enterprise environments. - Operate within Hillwood’s risk-tier governance framework, ensuring ARB review and lifecycle gates for Medium and High-risk solutions. RAG, Document Intelligence, and AI Platform Patterns: - Implement Retrieval-Augmented Generation (RAG) pipelines, document intelligence workflows, structured extraction workflows, and contextual intelligence patterns. - Build and maintain reusable components for document parsing, chunking, metadata enrichment, embeddings, retrieval, citations, validation, and source traceability. - Support the creation of technical standards for prompts, tools, agents, evaluators, test datasets, and deployment patterns. - Partner with data teams to connect AI workflows to enterprise data platforms, reporting layers, and business systems. Stakeholder Partnership and Pilot Governance Support: - Support discovery, scoping, and prioritization by advising on technical feasibility, implementation complexity, dependencies, risks, and support needs. - Support Steering Committee, division leadership, and ARB presentations by preparing technical architecture, demo environments, implementation status, risk considerations, and production readiness inputs. - Partner with Division AI Champions and business stakeholders to ensure solutions are usable, explainable, and aligned to business workflows. - Prepare or contribute to technical documentation, support readiness assessments, adoption materials, and scale recommendations. - Communicate technical concepts clearly to non-technical stakeholders when needed, while partnering with AI business solution leads on executive framing and ROI narratives. Required Skills and Abilities: - Ability to design, build, and support GenAI solutions, including LLM integration, prompt engineering, structured outputs, and Retrieval-Augmented Generation (RAG) pipelines. - Strong hands-on engineering capability using Python, REST APIs, Azure services, workflow automation tools, and enterprise integration patterns. - Ability to design and implement agent-based and multi-step workflows, including orchestration and execution of real business actions. - Ability to build reliable AI systems with validation, testing, observability, error handling, security awareness, and production supportability. - Ability to translate business-approved requirements into technical implementation plans, architecture diagrams, build tasks, and deployment plans. - Demonstrated ability to apply delivery discipline, including pilot gate methodology, documentation standards, quality validation, and lifecycle governance. - Ability to collaborate cross-functionally with business leaders, AI champions, governance bodies, IT architecture, security, and data teams to drive successful outcomes. - Proficiency in Python for LLM integration, API development, automation scripting, data processing, and evaluation workflows. Education and Experience: - Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Science, or related field. - Demonstrated delivery of production-grade GenAI solutions used by real business users. - Hands-on experience designing and building multi-step, agentic workflows using orchestration frameworks. - Experience with at least one major LLM platform (Claude, OpenAI, Azure OpenAI, or equivalent), including prompt engineering, function calling, and structured outputs. - Experience developing Retrieval-Augmented Generation (RAG) solutions and contextual intelligence pipelines. - Experience building APIs, cloud functions, workflow automations, data integrations, or backend services in an enterprise environment. - Azure platform experience, including Azure Functions, Logic Apps, Azure AI Studio, Azure OpenAI Service, Azure AI Search, or related services, preferred. - Experience with Power Automate or similar low-code workflow tools, preferred. - Experience with vector databases, document intelligence pipelines, or advanced RAG architectures, strongly preferred. - Exposure to enterprise data platforms and business systems such as Databricks, Power BI, JDE, Yardi, Salesforce, SharePoint, or Medius, a plus. - Familiarity with real estate, property management, financial services, lease management, AP automation, or property operations, a plus. - Consulting, internal innovation, or client-facing delivery experience is helpful, but deep hands-on delivery ownership is more important for this technical role. What Success Looks Like: - You are the person who turns approved AI opportunities into reliable, production-ready technical applications and business solutions. - You build the technical foundation that allows Hillwood to reuse and scale AI capabilities across divisions. - You earn trust through demonstrated value, clear communication, strong engineering discipline, and reliable solutions that work the first time. - You are comfortable operating in ambiguity, energized by real-world business challenges, and focused on shipping solutions that scale. - You grow into a technical owner for Hillwood’s enterprise AI patterns, solution accelerators, integrations, evaluation practices, and production support standards. We need to output HTML fragment with headings

      for sections we include: About the role, Responsibilities, Required Skills and Abilities (maybe Skills), Education and Experience (maybe Qualifications). Could also include What Success Looks Like as part of About the role or maybe as a separate section like "Qualifications"? But Qualifications is in list; we could treat Education and Experience as Qualifications. The "What Success Looks Like" could be part of About the role or maybe a separate section like "Qualifications"? But better to incorporate into About the role. Thus sections:

      About the role

      Company Overview: ...

      Position Summary: ...

      What Success Looks Like: ...

      maybe as separate

      but we could combine. But we need to preserve all details; we can include bullet list for What Success Looks Like as

      • . Similarly Responsibilities as
        • with subheadings? The responsibilities have subheadings (Technical Solution Design and Engineering, etc.) Should we keep those as headings? The instruction says use

          for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings inside? It says use

          for section headings (list). Likely they want only those top-level headings as h3. Subheadings could be

          or ? Not specified. Safer to keep them as

          ...:

          or just as part of list items. We can embed the subheading as plain text before the list. We need to output only HTML fragment, no markdown. Let's construct:

          About the role

          Company Overview: Hillwood, a Perot Company, is a premier real estate investment and development company founded on a culture of integrity, respect, excellence and teamwork. The company is a full-service real estate developer, investor and advisor focused on developing opportunities for investors, partners and communities around the world. See additional details at www.hillwood.com.

          Position Summary: Hillwood IT is seeking a hands-on AI Solutions Engineer to join our growing in-house IT team in Dallas, TX and help build the technical foundation for Hillwood's enterprise AI capability. This role is responsible for converting approved business use cases into secure, reliable, production-grade GenAI solutions, automations, agents, and RAG-enabled applications that deliver measurable business value. The AI Solutions Engineer will partner closely with AI Business Solutions Engineers, division stakeholders, IT architecture, security, data teams, and governance groups to transform approved AI opportunities into scalable production solutions. This is not a research-only or advisory role. Success is defined by delivering AI solutions that are technically sound, supportable, and trusted by business users while establishing reusable patterns that enable AI capabilities to scale across Hillwood. The ideal candidate is motivated by the opportunity to build more than isolated tools. This individual will help create the technical foundation, standards, and solution patterns that allow AI to become a trusted, repeatable, and high-value capability across the organization.

          Responsibilities