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About This Role
Description:
Valiant Residential is seeking a Senior AI Developer to own and build production AI applications for our multifamily operating company that will solve real operational challenges across our organization. Reporting directly to the VP of Technology, this is a senior- level individual contributor role for someone who thrives at the intersection of business processes, software development and applied artificial intelligence.
The most important qualification is judgment about business workflows & edge cases. You can sit with the people who do the work, see how a process actually moves, and build software that fits it. This person will own the applications: you learn the workflow, you build the system, and you stay with it after it ships.
You should bring a strong, practical understanding of modern AI development, including prompt engineering, model inference, agent design, tool and skill orchestration, guardrails, evaluation frameworks, and production deployment. Just as important, you know when AI is the right solution, when it is not, and how to deliver systems that are reliable, measurable, and aligned with business outcomes.
If you are energized by solving real-world operational problems, building AI applications that create measurable impact, and owning products from concept to production, we want to hear from you.
We Are Valiant
With over 37 years of experience, Valiant Residential is a next-generation property management leader, uniquely attuned to the needs of property owners and residents. We are driven by a Valiant Spirit—bold, innovative, and committed to excellence—fostering prosperous partnerships and long-term success. Our team thrives on collaboration, integrity, and strategic thinking, making a meaningful impact in the industry.
Why Work at Valiant?
At Valiant Residential, we don’t just offer jobs—we build careers. Consistently ranked as a "Top Place to Work," we are a leader in the property management industry, driven by bold, innovative professionals who make an impact.
We believe in rewarding great work with competitive total compensation and time off with comprehensive benefits, including free health insurance options and 401(k) matching. Growth isn’t just encouraged—it’s expected. With career development opportunities, a collaborative culture, and a commitment to innovation, we empower our employees to thrive.
What You’ll Be Doing
- Spend time with the people who own the workflow, whichever department they sit in. Map the steps, the exceptions, the handoffs, and the decision that matters.
- Turn that understanding into a scoped AI application: what the system does, what a person still decides, and how you will know it worked.
- Design and ship agents, copilots, and workflow automation that people rely on in their daily work, including the prompts and the skills those agents use.
- Implement tool calling, multi-step workflows, structured outputs, and retrieval over the documents and data the workflow already uses.
- Set guardrails for what the agent may do, what a person must approve, and how a bad action is stopped.
- Connect those systems to the applications the business runs on, including property management platforms.
- Evaluate agents before users depend on them, then deploy them. Own inference in production: quality, cost, security, logging, and a way to turn the AI path off.
- Review designs and code. Write the runbook and the explanation a non-engineer can follow.
- Handle the company's data with the care production use requires.
What you bring
- A track record of learning unfamiliar operation and shipping software that matches how the work is actually done.
- Strong, hands-on command of applied AI: prompting, model inference, skill creation, guardrails, agent evaluation, and deployment.
- 3 or more years building production software, with meaningful experience applying large language models or agent workflows to real tasks.
- At least one AI application you took from a working prototype to something operators use and stayed with after release.
- Strong programming skills, with Python as the language you are most effective in, and enough SQL to understand the data underneath a workflow.
- Production habits: version control, code review, testing, deployment, and enough observability to know when a job failed.
- The ability to explain a workflow and a system to an operator and to an engineer in the same week.
Requirements:
- Multifamily housing experience is heavily desired*. Broader property management or real estate experience is also valued.*
- Experience working with data, including data quality, validation, and structuring data to support AI and automation solutions.
- Ability to operate independently and partner effectively with leaders, managers, and operations teams across the organization.
- Yardi, RealPage, Entrata, or another property management platform.
- A major cloud platform. Valiant runs this work on Microsoft Azure, including Azure OpenAI and Azure AI Foundry.
- A specific agent framework or document-understanding pipeline you have already used.
If you have a Valiant Spirit—bold, driven, and ready to make an impact—we want you on our team.
Third-Party Submissions & Staffing Agencies
Valiant Residential does not accept unsolicited resumes or candidate submissions from staffing agencies or third-party recruiters for any posted positions. Any unsolicited resumes received will be considered property of Valiant Residential, and no placement fees will be paid. All official recruiting communications from Valiant Residential will come from an @valiantresidential.com email address. If you receive outreach from a different email domain claiming to represent us, please verify the legitimacy of the communication before responding.
Americans with Disabilities Act (ADA) Accommodation Statement
Valiant Residential is committed to complying with the Americans with Disabilities Act (ADA) and ensuring equal opportunity in employment for qualified persons with disabilities. We provide reasonable accommodations to enable individuals with disabilities to perform essential job functions, participate in the hiring process, and enjoy the benefits and privileges of employment.
If you require a reasonable accommodation to apply for a position or to perform your job, please contact [email protected]. Requests will be reviewed on a case-by-case basis in accordance with applicable laws.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Valiant Residential, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Valiant Residential AI Hiring
Valiant Residential has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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