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About This Role
Position Summary
Hirschi Companies is a multi-entity construction and asset enterprise operating across Nevada, Arizona and Utah. Hirschi has invested in an Enterprise Intelligence Platform built on Microsoft Fabric: a lakehouse with medallion architecture spanning all operating entities, a unified semantic model, and executive dashboards used in daily decision-making. The platform was built with an implementation partner, and ownership is now moving in-house. The Enterprise Data & AI Architect is the person it moves to.
This is a hands-on builder role at the intersection of data engineering, business analysis and applied AI. AI and automation carry equal weight with platform ownership. The platform is already built; the bar is that everything the implementation partner can do on it, you can do. Completing knowledge transfer and running the platform independently is the first order of business.
You will report to the VP of Technology, who owns the relationships with executive leadership and the business presidents; requirements and priorities reach you through that channel. Within that structure you have real ownership: architecture decisions, tooling decisions, and the roadmap recommendation for what the platform does next.
Essential Job Responsibilities
- Own the Microsoft Fabric Enterprise Intelligence Platform end to end: Bronze, Silver and Gold medallion layers on Delta Lake across all operating entities, with documented lineage and entity relationships.
- Complete knowledge transfer from the implementation partner and operate the platform independently; the bar is that everything the partner can do on the platform, you can do.
- Build, operate and extend data pipelines and notebooks (PySpark, SQL) ingesting from ERP, project, CRM, time tracking, fleet, safety and work management systems, plus historical financial data from legacy platforms; ERP additions (new entities, modules and fields) are absorbed without rework.
- Bring new source systems and new ERP data onto the platform end to end: pipeline build, semantic model extension, and the new Power BI dashboards that come with them, from requirements through production.
- Run the data quality framework: validation tests, reconciliation to the ERP and the monthly close, and a variance log leadership can trust.
- Operate pipeline monitoring, alerting and break-fix in-house; manage Fabric capacity, cost and performance.
- Maintain CI/CD through Azure DevOps Git across dev, UAT and production, with code review and environment promotion as standard practice.
- Own the unified Power BI semantic model: star schema, DAX measures, time intelligence, and documentation kept AI-ready with business-friendly naming.
- Maintain and extend the executive dashboards, reconciled to source with no manual adjustment, and design and build new dashboards and report pages with drill-down from enterprise to entity to job.
- Implement and maintain row-level security tied to Entra ID groups, with data classification applied to PII, payroll and financial data before anything goes live.
- Participate in the vendor-led SharePoint buildout: digest the knowledge transfer, give input on structure, metadata and data-related applications, and own feeding SharePoint data into Fabric.
- Integrate SharePoint, monday.com and Fabric so project data flows without duplicate entry.
- Document the business process for each division and partner with the VP of Technology to find and prioritize the processes worth automating with AI.
- Translate operational questions into KPIs, models and reports, working from requirements the VP of Technology gathers and prioritizes with business leadership.
- Identify business problems AI can solve, then build and ship the solutions: document processing, workflow automation, agents over governed data.
- Build Hirschi’s first machine learning, forecasting and prediction models on the Fabric platform.
- Build process automations across Microsoft 365, monday.com and operational systems that remove manual re-entry and duplicate work.
- Support AI governance: usage policy, data handling rules, role-based access to confidential data, and adoption that is measured rather than assumed.
- Drive adoption through training and role-based enablement, and maintain platform documentation so the enterprise never depends on what lives in one person’s head.
Must Have - Required Qualifications
- 7+ years in data engineering, business intelligence or analytics engineering, including 2+ years hands-on with Microsoft Fabric or a closely comparable lakehouse platform (Databricks, Synapse) with a fast ramp to Fabric.
- At least 2 enterprise-level platform buildouts (a lakehouse or BI platform serving multiple business units or entities) carried end to end, from architecture through production operation. Work samples are required: sanitized architecture diagrams, semantic models or dashboards, in a portfolio or a live walkthrough.
- Deep experience with medallion architecture, Delta Lake, and pipeline development in PySpark and SQL.
- Expert-level Power BI: semantic model design, DAX, row-level security, performance tuning, and dashboards non-technical leaders actually use.
- At least one current Microsoft Fabric certification (DP-600 Fabric Analytics Engineer or DP-700 Fabric Data Engineer), or earned within the first 90 days of hire.
- Demonstrated applied AI experience: AI solutions built and shipped against business problems (automation, document processing, agents, forecasting), with AI-assisted development as part of daily work.
- Strong business analysis skills: gathering requirements, documenting business processes, and explaining technical tradeoffs in business terms.
- Working knowledge of SharePoint and the Power Platform (Lists, Power Apps, Power Automate) sufficient to give informed input on data applications and integrate SharePoint data into the platform.
- Experience with Git-based CI/CD, preferably Azure DevOps, across dev, UAT and production environments.
- Self-directed: able to own an enterprise platform without a large team around you.
- Must be authorized to work in the United States.
- Must pass a mandatory drug test and background check.
- Must pass Hirschi’s Driver Approval Process.
- Must be able to read and write in English at a level sufficient to follow instructions, complete documentation, and communicate effectively.
- Must be able to lift 25 lbs.
Nice to Have - Preferred Qualifications
- Machine learning and forecasting: hands-on experience building and deploying prediction models in Python (Fabric data science workloads, Azure ML or similar). Nothing predictive exists at Hirschi yet, so candidates who bring this are strongly preferred.
- Additional Microsoft certifications beyond the required Fabric certification: the second of DP-600/DP-700, PL-300 (Power BI), AI-102 (Azure AI Engineer).
- Construction, real estate or asset-heavy industry experience, especially with ERP data from Viewpoint Vista or similar, and platforms like Procore.
- Microsoft Purview or comparable data governance and classification tooling.
- Experience integrating work management platforms (monday.com or similar) by API.
- Experience taking over a platform from a consulting partner and running it as the in-house owner.
- Bilingual (English/Spanish).
Core Competencies
- Technical Excellence: Builds and runs reliable, well-documented data and AI infrastructure with attention to quality and scalability.
- Analytical Thinking: Identifies root causes quickly and applies structured problem-solving to complex data issues.
- Ownership: Takes full accountability for platform uptime, data quality, and architecture decisions, with no vendor dependency as the default path.
- Communication: Frames technical decisions in business terms the VP of Technology can carry to leadership.
- Collaboration: Partners effectively with the VP of Technology, vendors, and cross-functional teams.
- Discipline: Follows proper sequencing and governance standards without shortcuts.
Join us to be at the forefront of technological innovation! This role offers an exciting opportunity to influence our enterprise data & AI architecture landscape while working with cutting-edge tools and methodologies that shape the future of our organization’s digital journey.
Pay: $145,000.00 - $170,000.00 per year
Benefits:
- 401(k)
- Dental insurance
- Employee assistance program
- Health insurance
- Life insurance
- Paid time off
- Retirement plan
- Vision insurance
People with a criminal record are encouraged to apply
Work Location: In person
Salary Context
This $145K-$170K range is in the lower quartile for AI Architect roles in our dataset (median: $183K across 30 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 8,943 AI roles we're tracking, AI Architect positions make up 1% of the market. At Hirschi Companies, LLC, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $230,000 based on 212 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($157K) sits 32% below the category median. Disclosed range: $145K to $170K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Hirschi Companies, LLC AI Hiring
Hirschi Companies, LLC has 1 open AI role right now. They're hiring across AI Architect. Based in North Las Vegas, NV, US. Compensation range: $170K - $170K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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