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About This Role
Department:
WDTech \- R\&D
We are Walker \& Dunlop. We are one of the largest providers of capital to the commercial real estate industry, enabling real estate owners and operators to bring their visions of communities — where people live, work, shop, and play — to life. We are committed to creating meaningful social, environmental, and economic change in our communities.
Department Overview
WDTech is W\&D’s in\-house technology team – a group of collaborative and highly skilled technology professionals, all of whom are leading experts in real estate data, data science, and technology.
WDTech Product works hand\-in\-hand with the business to translate business outcomes into actionable product strategies, aligning user needs with innovative solutions, via close collaboration with our engineering partners, to ensure W\&D maintains a competitive advantage as our technology landscape and target market rapidly evolve.
The Impact You Will Have
The AVP, Senior Product Manager – Agentic Workflows is a senior individual contributor responsible for identifying, developing, and delivering AI\-powered workflow solutions that improve business processes across Walker \& Dunlop. Reporting to the Chief Product Officer, this role partners closely with business leaders and cross\-functional teams to identify operational challenges, rapidly prototype solutions, and guide them through production deployment. Success in this role requires combining product management expertise with hands\-on experience using modern AI technologies to deliver measurable business outcomes.
Primary Responsibilities
- Partner with business teams to identify workflow challenges and opportunities for AI\-enabled automation through direct user engagement and observation.
- Travel regularly to Walker \& Dunlop offices and field locations to understand business processes and gather insights that inform product development.
- Translate business needs into product requirements and maintain a prioritized backlog of AI workflow initiatives.
- Design, build, test, and iterate AI workflow prototypes using modern AI tools and development platforms.
- Present solutions to stakeholders, gather feedback, and refine workflows based on user input.
- Partner with Engineering, Site Reliability Engineering (SRE), Information Security, Legal, Procurement, and other cross\-functional teams to transition solutions from prototype to production.
- Define, measure, and report on key performance metrics, including adoption, operational efficiency, quality improvements, and business impact.
- Maintain workflow documentation, evaluation records, and audit artifacts to support governance and scalable AI development practices.
- Communicate project priorities, progress, and outcomes to business stakeholders and executive leadership.
- Contribute to the evolution of Walker \& Dunlop's AI product capabilities by identifying reusable solutions, improving development practices, and sharing knowledge across teams.
- Perform other duties as assigned
Education and Experience
- Bachelor's degree in Business, Computer Science, Information Systems, Engineering, or a related field, or an equivalent combination of education and experience.
- 8\+ years of product management experience with a demonstrated track record of delivering AI, machine learning, or intelligent automation solutions into production.
- Experience managing products throughout the full lifecycle, from discovery and solution design through implementation and adoption.
- Experience collaborating with software engineering teams and translating business needs into technical requirements.
- Experience within commercial real estate, mortgage finance, financial services, or another regulated industry is preferred.
- Familiarity with developer tools such as Git, GitHub/GitLab, Python, JSON, YAML, and Markdown is preferred.
Knowledge, Skills and Abilities
- Strong knowledge of AI technologies, including large language models (LLMs), prompt engineering, retrieval\-augmented generation (RAG), and agentic workflows.
- Ability to independently prototype AI\-enabled solutions using modern AI development tools and platforms.
- Strong product management, analytical, and problem\-solving skills with the ability to translate complex business challenges into scalable technology solutions.
- Excellent written and verbal communication skills, with the ability to communicate effectively with both technical and non\-technical stakeholders.
- Strong collaboration and influencing skills, with the ability to build partnerships across business and technology teams.
- Ability to manage multiple priorities in a fast\-paced environment while maintaining high\-quality execution and documentation.
- Willingness and ability to travel regularly to Walker \& Dunlop offices and field locations.
- Ability to show ownership of your work, take on challenges and acknowledge growth opportunities, and demonstrate patience when learning new processes
- Courtesy, respect, and thoughtfulness in teaming with colleagues and other stakeholders
This position has an estimated base salary of $170,000 \- $190,000 plus a discretionary bonus. An employment offer is based on the applicant’s relevant work experience, applicable knowledge, skills, abilities, internal equity, and alignment with market data.
\#LI\-MR1
\#LI\-Remote
What We Offer
- The opportunity to join one of Fortune Magazine’s Great Places to Work winners
- Comprehensive benefit options\* that have earned Walker \& Dunlop the gold level of the 2025 Cigna Healthy Workforce Designation™, some of which include:
+ Up to 83% subsidized medical payroll deductions
+ Competitive dental and vision benefits
+ 401(k) \+ match
+ Pre\-tax transit and commuting benefits
+ A robust health and wellness program – earn cash rewards and gain access to resources that
promote health, engagement, and balance
+ Paid maternity and parental leave, as well as other family paid leave programs
+ Company\-paid life, short and long\-term disability insurance
+ Health Savings Account and Healthcare and Dependent Care Flexible Spending
- Career development opportunities
- Empowerment and encouragement to give back – volunteer hours and donation matching
- Eligibility may vary based on average number of hours worked
Equal Employment Opportunity Statement
We are committed to equity in all steps of the recruitment and employment experience. We believe in equal access to opportunities in our workplace. We do not tolerate discrimination, including harassment, based on any characteristic protected by applicable law, such as race, color, national origin, religion, gender identity, sexual orientation, sex, age, disability, veteran or military status, and genetic information, or any other characteristic protected by applicable law. We strive to be a safe place to ask questions, build professional relationships, and develop careers.
SPAM
Please be wary of recruitment scams. An indication of a scam might be a request for sensitive or bank information at the time of application or emails coming from a non walkerdunlop.com email address. Please call us at 301\.215\.5500, if you have any concerns about information requested during or after the application process.
Fair Chance Hiring
Background checks, including any questions related to infractions, arrests, or conviction records, will not be conducted until after a conditional offer of employment has been accepted. We will consider for employment qualified applicants regardless of arrest and conviction records, in accordance with federal, state, and local laws.
Salary Context
This $170K-$190K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Walker & Dunlop, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $170K to $190K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Walker & Dunlop AI Hiring
Walker & Dunlop has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $190K - $190K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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