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About This Role
At Cotality, we are driven by a single mission—to make the property industry faster, smarter, and more people\-centric. Cotality is the trusted source for property intelligence, with unmatched precision, depth, breadth, and insights across the entire ecosystem. Our talented team of 5,000 employees globally uses our network, scale, connectivity and technology to drive the largest asset class in the world. Join us as we work toward our vision of fueling a thriving global property ecosystem and a more resilient society.
Cotality is committed to cultivating a diverse and inclusive work culture that inspires innovation and bold thinking; it's a place where you can collaborate, feel valued, develop skills and directly impact the real estate economy. We know our people are our greatest asset. At Cotality, you can be yourself, lift people up and make an impact. By putting clients first and continuously innovating, we're working together to set the pace for unlocking new possibilities that better serve the property industry.
Job Description:
We are seeking a highly motivated and self\-driven AI Engineer with a strong background in building scalable Gen AI/Agentic AI data engineering solutions. The ideal candidate brings deep expertise in Google Cloud Platform (GCP), with hands\-on experience in Gen AI/Agentic AI Engineering, Google Doc AI, BigQuery, Postgres and Python. Familiarity with Data Engineering technologies like Dataflow (Apache BEAM), DataProc (Apache Spark) and orchestration tools like Apache Airflow is essential.
We are executing a major AI overhaul across our core enterprise data pipelines. Every day, massive volumes of unstructured document images flow through our ingestion engines. We are modernizing this entire supply chain using advanced Document AI, Gemini LLMs, and Agentic workflows to drive straight\-through processing.
*Key Responsibilities*
- Design and implement scalable, high\-performance Document Image extraction pipelines using Google Doc AI (including Custom Extractors, Classifiers, and Splitters), ensuring optimal integration, reliability, and incorporating Human\-in\-the\-Loop (HITL) fallback workflows.
- Translate business requirements and processes into well\-architected technical solutions, aligning with enterprise architecture standards and long\-term technology strategy
- Define and document solution architecture, technical designs, and requirements to support business objectives while balancing practical implementation and strategic vision
- Provide technical leadership and guidance to engineering and support teams, ensuring alignment with business and IT strategies
- Develop and present architecture and design artifacts to both technical and non\-technical stakeholders
- Proactively identify challenges, assess downstream impacts across business domains, and drive solution design through research, validation, and stakeholder consensus
- Apply industry and domain best practices, offering architectural guidance and strategic input to business and technology leadership
- Evaluate the impact of new solutions on existing architecture, ensuring compliance with enterprise standards and governance frameworks
- Contribute to enterprise\-wide technology initiatives and support critical issue resolution when needed
- Actively participate in team ceremonies, provide status updates, and support overall project governance and execution
- Mentor and coach junior engineering team members to foster professional growth.
Job Qualifications:
- Bachelor’s degree in computer science, Engineering, or a related discipline (or equivalent work experience)
- 7\+ years of experience in IT, with strong expertise in Gen AI/Agentic AI, Document Image extraction, data architecture, Kubernetes/GKE, and driving continuous improvements—preferably within the mortgage, real estate, finance or insurance domains
- Deep understanding of Gen AI/Agentic AI, Machine Learning, Unstructured Data Extraction using hybrid AI/ML, Prompt Engineering, Multi Agent Orcherstration, RAG, Model surveillance/Governance, LLM Token optimization techniques to support strategic architecture planning
- Deep, hands\-on expertise with the Google Document AI suite (Form Parser, Custom Document Extractors (CDE), CDE training/tuning) and evaluating extraction model performance (Precision/Recall optimization)
- Proven experience designing Human\-in\-the\-Loop (HITL) workflows that are critical for Enterprise Document AI pipelines
- Strong knowledge of the full lifecycle of Gen AI/ML applications, data engineering pipelines design and development
- Proven experience in enterprise application architecture, including governance, standards, and strategy development
- Demonstrated ability to resolve complex enterprise\-wide application \& data architecture challenges
- Expertise in designing solutions aligned with strategic technology roadmaps and emerging industry trends
- Proficiency with data modeling tools and strong experience across SQL and NoSQL technologies
Technical Skills \& Experience
- AI \& Document Processing: Hands\-on experience with Google Document AI, Prompt Engineering, and AI\-native development frameworks or lifecycle methodologies (e.g., BMAD, Spec Kit).
- Cloud \& Data Stack: Deep GCP expertise (AWS/Azure a plus), paired with strong hands\-on skills in Python, PySpark, and modern graph databases (e.g., Neo4j).
- Big Data \& Orchestration: Proven experience building pipelines using distributed processing (Spark, Hadoop, Elasticsearch) and orchestration platforms (Google Cloud Dataflow, Apache Beam, Apache Airflow).
- Architecture \& Design: Ability to translate complex requirements into detailed technical designs, specifications, and enterprise system architecture.
Leadership \& Soft Skills
- Technical Leadership \& Mentorship: Proven ability to guide engineering teams, drive architectural best practices, and elevate code quality through active mentorship, reviews, and collaborative pairing .
- Stakeholder \& Vendor Ownership: Strong communication skills with the ability to bridge business requirements, challenge vendor approaches, and align technical strategy with executive stakeholders .
- Execution \& Delivery: Ability to navigate complex, multi\-phase delivery roadmaps and balance competing priorities in a dynamic environment .
Preferred Qualifications
- AWS or Azure experience (to complement GCP)
- Real Estate, Mortgage, Finance, or Insurance domain knowledge
- Modern graph databases (e.g., Neo4j)
- Event\-driven architecture or streaming frameworks (e.g., Kafka)
Annual Pay Range:
134,400 \- 185,000 USD
Application Window:
This opportunity is expected to remain posted through the date identified below, subject to business needs.
2026\-08\-06
Thrive with Cotality
At Cotality, we offer more than just a job, we provide a benefits experience designed to support your whole self. From a flexible working model to competitive time off and standout health coverage with meaningful perks and growth opportunities, our package is built to help you thrive at work and in life.
Highlights, depending on role classification, include:
- Time off: Generous PTO and 11 paid holidays, plus well\-being and volunteer time off.
- Family Support: Up to 16 weeks of fully paid parental leave and a baby stipend.
- Health: Multiple medical plan options with mental health and wellness support offerings.
- Retirement: 401(k) with company match and vesting after one year.
- Financial Perks: $400 annual well\-being stipend and tuition assistance up to $5,250\.
- Extras: Recognition Rewards, Referral bonuses, exclusive discounts and more!
Cotality is an Equal Opportunity employer committed to attracting and retaining the best\-qualified people available, without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, disability or status as a veteran of the Armed Forces, or any other basis protected by federal, state or local law. Cotality maintains a Drug\-Free Workplace.
Cotality is fully committed to a work environment that embraces everyone’s unique contributions, experiences and values. We offer an empowered work environment that encourages creativity, initiative and professional growth and provides a competitive salary and benefits package. We are better together when we support and recognize our differences.
By providing your telephone number, you agree to receive automated (SMS) text messages at that number from Cotality regarding all matters related to your application and, if you are hired, your employment and company business. Message \& data rates may apply. You can opt out at any time by responding STOP or UNSUBSCRIBING and will automatically be opted out company\-wide.
Salary Context
This $134K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Cotality, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($159K) sits 26% below the category median. Disclosed range: $134K to $185K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Cotality AI Hiring
Cotality has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Dallas, TX, US, Milwaukee, WI, US. Compensation range: $130K - $190K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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