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About This Role
Director of AI Workflow and Brokerage Systems
Location: Dallas, TX (on-site, Texas office)
Employment type: Full-time, W-2 from day one
Pay range: $135,000–$165,000
First 120 days: Introductory period at the posted pay. At day 120, we decide whether employment continues. That decision is mutual. Group health is included.
We are a commercial real estate brokerage firm operating nationwide. You’ll design workflows and systems, and then build them. This role owns how work moves through the company: Salesforce, data, marketing production, and AI. Commercial real estate can be learned here.
The point of the job is that data and work move cleanly, people can stay in Salesforce, and listings still ship. Custom software and durable integrations are how that happens. When the work is in the right place, the extra tools fall away. That shorter stack is the result, not the assignment.
Two facets, one job
Facet 1: Salesforce and AI
You’ll own this work. The core of the job is to design and implement the next version: data feeds, matching rules, Flow and other automation, integrations, and AI in the UI so the team works in the org and the extra tools fall away.
You’ll need ample experience in coding agents such as Cursor, Claude Code, or Codex, used on real files and real systems, inside a real project. ChatGPT or Claude in a browser, used only as a chat window, is not enough.
Salesforce admin is part of the job because you run the org yourself. That means objects, fields, validation, permissions, and Flow that can handle branching logic and variables. A click-only admin background, with no design and no build, is not this role.
You’ll need working experience with git, Google Cloud Storage (GCS) or similar object storage, and APIs used to create data handshakes between systems. The job is durable integrations, not one-off exports.
Examples of the work:
- Stand up a feed of ownership and contact data into Salesforce. AI compares sources with robust matching, looks for two sources that corroborate, and only then writes the fields.
- Use existing Salesforce email as context when drafting further correspondence, and integrate Zoom Phone so call context lives on the record. Email is already connected. Zoom Phone is not, and that build is part of the job.
Facet 2: Marketing
You’ll run the day-to-day marketing workflows, but a core part of the job is to design and implement a successor that is simpler for the team and stronger underneath.
A deal still needs an offering memorandum, a SendGrid blast, and a post on Crexi and LoopNet. Listings still have to ship during the rebuild.
Salesforce and Dropbox are the source. From there the shop still produces the OM (InDesign templates today, more AI-assisted later), the blast, the marketplace post, and the site update. Today that also uses make.com, WebMerge, Batch Data, Apify, Render, Adobe, and the website. The resume does not need every tool listed here. What is required is keeping today’s process working and designing what it should become, including custom software we already run on Replit for documents and workflow.
Across both facets, about 60% of the time keeps today’s paths working, and about 40% designs and ships the next ones. That split gets revisited at six months.
Responsibilities
- Map, fix, and own workflows across Salesforce, files, email, and marketplaces
- Administer Salesforce and build in Flow, including branching logic that changes with variables
- Use Salesforce CLI for exports, queries, and careful upserts. If Salesforce CLI (sf) is new, it is expected within the first two weeks
- Write practical SQL when the answer lives in a table
- Use git, GCS or similar object storage, and APIs to move data between systems
- Build with AI agents (Cursor, Claude Code, Codex, or the same class of tool) as the default, including small changes in our Replit apps
- Keep listings shipping: OM out, blast out, Crexi and LoopNet up, files in the right Dropbox folder
- InDesign is a plus. Running existing templates is enough. This is not a designer role
A good fit
- Resourceful. Fixes the workflow, the folder, or the org before asking for a new tool
- About 3 to 5 years of systems, integrations, or workflow work that can be shown, including something designed and shipped, not only a deck or an admin ticket queue
- Ample hands-on experience with Cursor, Claude Code, Codex, or the same class of tool, on real projects
- Has designed a workflow or integration and shipped it, including the rules for when data is written and when it is held
- Runs Salesforce hands-on, including Flow and Salesforce CLI
- Experience with git, GCS or similar, and APIs for system-to-system data handshakes
- Can write a SELECT and a safe UPDATE, and does not write to production cold
- No commercial real estate background is fine
Not a fit
- AI experience is only ChatGPT or Claude in a browser, with no work in an agent or a project
- Wants to train models, or wants a large team and a perfect ticket queue
- Knows Salesforce only as clicks and does not want a terminal or Flow
- Has never used git, an API, or object storage, and does not want to
- Wants to design brand all day, or will not do production work
- Treats “turn off tools” as the project, instead of making the work reliable in Salesforce
How we hire
A short screen, then W-2 employment. The first 120 days are the evaluation. There is no unpaid exercise and no separate trial rate.
1. Screen (about 30 minutes)
Salesforce Flow and admin experience, how coding agents are used day to day, and a walkthrough of a branching workflow the candidate has actually built.
2. The first 120 days
You start full time, W-2, at the posted pay. The work is the job: Salesforce, listings, and the next version of the workflows. At day 120, we decide whether employment continues. We look at whether data and work move cleanly, whether listings still ship, and whether changes are reviewed before they touch production.
Apply with two artifacts: a workflow or system designed end to end, and something shipped with Cursor, Claude Code, Codex, or the equivalent.
Pay: $135,000.00 - $165,000.00 per year
Benefits:
- Health insurance
Work Location: In person
Salary Context
This $135K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Peranich Huffman Net Lease Group, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($150K) sits 29% below the category median. Disclosed range: $135K to $165K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Peranich Huffman Net Lease Group AI Hiring
Peranich Huffman Net Lease Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in TX, US. Compensation range: $165K - $165K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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).
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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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