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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 an experienced Senior Business Intelligence Engineer (AI‑Native Analytics) to join our team in Dallas, TX to play a high‑visibility role at the center of the company’s data strategy. This position is responsible for designing and delivering executive‑grade analytics used by executive and senior leadership, translating complex real estate and financial data into clear, actionable insights that support critical decision‑making.
The Senior BI Engineer will blend deep business intelligence engineering expertise with strong business acumen and modern, AI‑enabled analytics. Serving as a trusted partner to executive stakeholders, this role will lead discovery conversations, shape reporting requirements, and ensure every deliverable meets board‑level standards for accuracy, clarity, and design quality. The ideal candidate brings both technical depth and executive polish, discerning when a traditional dashboard is sufficient and when AI‑powered, conversational, or embedded analytics deliver greater value. They ask strong business questions, translate ambiguity into clarity, and care deeply about both the substance and presentation of the insights delivered.
Responsibilities:
*Executive Analytics and Dashboard Delivery:*
- Design and build executive\-facing dashboards using Power BI, Databricks AI/BI (Lakeview), and other appropriate solutions based on audience and use case.
- Lead design reviews and approve mockups with senior stakeholders prior to build.
- Ensure visual hierarchy, data accuracy, performance, and usability across all BI outputs.
- Maintain a reusable library of certified dashboards, report components, and design standards.
- Develop BI center\-of\-excellence practices, including self\-service standards and guardrails
- Enable conversational and AI\-assisted analytics experiences (e.g., Databricks Genie Spaces, chat\-to\-chart, voice\-to\-insight).
- Build and deploy Databricks Applications (Lakehouse Apps / Mosaic AI) to embed analytics into operational workflows.
*Data Modeling and Semantic Layer:*
- Build and maintain Power BI semantic models aligned to certified enterprise metrics.
- Design star schemas and dimensional models serving as single sources of truth.
- Partner with data engineering on Databricks pipelines and report\-ready data structures.
- Implement row\-level security and access controls in alignment with governance policies.
*Stakeholder Engagement:*
- Facilitate requirements sessions with C\-suite and division leaders to define KPIs and reporting cadence.
- Translate qualitative executive feedback into clear technical specifications.
- Present dashboards and data narratives to senior leadership with confidence and clarity.
- Act as a subject matter expert on BI standards, design patterns, and best practices.
*Governance and Quality Assurance:*
- Enforce data governance, metric certification, and lineage documentation across BI assets.
- Validate equivalence between source systems (e.g., JDE, OneStream, Yardi, Salesforce) and published reports.
- Maintain documentation for report logic, DAX measures, and semantic model decisions.
Required Skills and Abilities:
- Advanced proficiency in Power BI, including DAX, semantic data modeling, row‑level security, paginated reports, and deployment pipelines.
- Strong capability working within Databricks AI/BI environments, including Lakeview dashboards, Genie Spaces, and Databricks Applications.
- Deep understanding of dimensional modeling principles, semantic layer design, and enterprise metric consistency.
- Strong ability to design executive‑grade dashboards that prioritize clarity, accuracy, performance, and visual storytelling.
- Ability to establish and apply BI standards, data quality expectations, and governance guardrails across analytics assets.
- Demonstrated ability to translate ambiguous business questions into clearly defined KPIs, reporting requirements, and technical solutions.
- Strong communication and stakeholder partnership skills, with the ability to influence outcomes through clarity, credibility, and judgment.
- Sound judgment in selecting appropriate analytics approaches, ranging from traditional dashboards to AI‑assisted or embedded analytics solutions.
Education and Experience:
- Bachelor’s degree in IT, Computer Science or related field.
- 5\+ years of experience in business intelligence, analytics, or related discipline.
- Experience supporting executive\-level or C\-suite reporting.
- Experience in real estate or investment\-driven reporting environments, preferred.
- Exposure to enterprise systems such as JDE, OneStream, Yardi, or Salesforce, preferred.
- Relevant certifications (PL\-300, Databricks, or equivalent experience), a plus.
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.
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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. Senior-level AI roles across all categories have a median of $227,400.
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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