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About This 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 Business Solutions Engineer to join our growing in‑house IT team in Dallas, TX to execute our deliberate transition from ad‑hoc AI experimentation to a governed, high‑value enterprise AI capability. The AI Business Solutions Engineer is a critical, hands\-on delivery role responsible for turning real business problems into working GenAI solutions that demonstrate measurable value.
This is not a research, advisory, or purely technical engineering role; you will work directly with business leaders across Hillwood’s Industrial, Residential, Commercial, and HKN Energy divisions—owning the full lifecycle from discovery through delivery, demonstration, and adoption. Success in this role is defined by solutions that ship, scale, and are trusted by the business.
Responsibilities:
*Business Discovery and Solution Design:*
- Conduct structured discovery sessions with division leaders and business stakeholders to identify, scope, and prioritize AI use cases.
- Translate ambiguous business problems into clearly defined GenAI solution designs with success metrics and pilot gate criteria.
- Facilitate working sessions with non\-technical stakeholders, making AI approachable and practical.
- Document solution designs at a level suitable for Architecture Review Board (ARB) review and pilot approval.
*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, and Medius.
- Own solution quality, including prompt engineering, output validation, error handling, 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.
*Stakeholder Engagement and Pilot Governance:*
- Present solution demonstrations to Steering Committee members and division leadership at launch, mid\-point, and scale decision gates.
- Communicate technical concepts clearly to non\-technical executives, including ROI framing, risk considerations, and capability explanations.
- Prepare use case gate materials such as ROI evidence, adoption metrics, support readiness assessments, and scale or retire recommendations.
- Partner with Division AI Champions to drive adoption through training, documentation, and hands\-on enablement.
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 business and stakeholder acumen, including discovery facilitation, solution scoping, ROI framing, and executive‑level communication.
- Ability to design and implement agent‑based and multi‑step workflows, including orchestration and execution of real business actions.
- Ability to integrate AI solutions with enterprise platforms, using Python, REST APIs, Azure services, and core business systems.
- 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, and governance bodies to drive adoption and successful outcomes
- Proficiency in Python for LLM integration, API development, and automation scripting
Education and Experience:
- Bachelor’s degree in Computer Science, Information Systems, 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.
- Consulting background (Big 4, boutique AI/digital consultancy, or internal innovation role with client\-facing delivery ownership), strongly preferred.
- Azure platform experience, including Azure Functions, Logic Apps, Azure AI Studio, or Azure OpenAI Service, preferred.
- Experience with Power Automate or similar low\-code workflow tools, preferred.
- Familiarity with real estate, property management, or financial services processes such as lease management, AP automation, or property operations, preferred.
- Experience with vector databases, document intelligence pipelines, or advanced RAG architectures, a plus.
- Exposure to enterprise data platforms and business systems such as Databricks, Power BI, JDE, Yardi, Salesforce, or SharePoint, a plus.
What Success Looks Like:
- You are the person who translates business problems into AI solutions and then actually builds and delivers them.
- You earn trust through demonstrated value, clear communication, 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.
EEO Statement:
Hillwood is committed to providing Equal Opportunity in Employment, to all applicants and employees regardless of race, color, religion, gender, age, national origin, military status, veteran status, handicap, physical or mental disability, sexual orientation, gender identity, genetic information or any other characteristic protected by law.
\#CORP
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Hillwood Development Company, LLC., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Hillwood Development Company, LLC. AI Hiring
Hillwood Development Company, LLC. has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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