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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:
About Cotality
Cotality is the insurance industry's leading provider of property intelligence, risk analytics, and workflow solutions. Our data powers underwriting, claims, and catastrophe risk decisions for the nation's largest carriers. We are building the AI\-first data foundation that defines how the industry consumes property intelligence, and this role sits at the center of that transformation.
Role Summary
We are looking for a Senior AI Architect to design and deliver AI systems across Cotality's property intelligence platform. This is a hands\-on individual contributor role with broad scope spanning internal agentic systems that power property analytics and decision workflows, and external AI integration architecture that makes Cotality's data products consumable by AI agents, foundation model platforms, and enterprise developer ecosystems.
You will work across both layers, ensuring they are coherent, secure, and built to scale with the growth of our product portfolio. You will define the standards, build the foundational components, and be accountable for the architecture working in production under real client load.
Key Responsibilities
*Technical*
- Design and build agentic AI systems, including multi\-agent frameworks, orchestration layers, memory and retrieval architectures, and tool\-based reasoning pipelines that operate against structured and unstructured property data.
- Own the external AI integration architecture, API gateway configuration, MCP server patterns, authentication and authorization flows, tool schema standards, and the reference architecture that product teams follow to expose their APIs as agent\-consumable tools.
- Pioneer Agent Experience (AX) design as a first\-class methodology for the organization analogous to UX or Developer Experience (DX). Treat AI agents as primary consumers of our systems and ensure that APIs, tool descriptions, and data outputs are optimized for LLM comprehension, context limits, and deterministic reasoning.
- Establish and enforce technical standards for how AI agents consume Cotality's data products, with a focus on tool description quality, input and output contracts, error handling patterns, and response metadata standards all evaluated through the lens of AX.
- Architect security and data provenance controls across the integration layer, including JWT claim schema design, defense\-in\-depth authorization patterns, audit logging, and response boundary enforcement.
- Design and implement observability and telemetry for AI systems to monitor token consumption, latency, error rates, prompt drift, LLM costs, and response quality in production.
- Establish CI/CD pipelines and evaluation frameworks for AI agents that measure accuracy, hallucination rates, and performance regressions before changes reach production.
- Optimize AI workload architecture by designing deployment strategies that decouple large model weights from application code, utilizing optimized base images and dynamic runtime mounting to maintain fast, reliable CI/CD pipelines.
- Scale inference and orchestration by architecting high\-throughput AI backends using specialized model servers such as vLLM or Triton on Kubernetes, with support for dynamic batching, streaming responses, and concurrent execution.
- Align application design with cloud economics by partnering with platform engineering to build cost\-aware AI systems, and designing agentic workflows that gracefully handle cold\-start latencies and infrastructure scaling events such as scale\-to\-zero or Spot instance evictions without dropping requests.
- Bring strong backend engineering practices to the AI layer, with a consistent track record of delivering production\-quality, maintainable code in cloud or containerized environments.
*Leadership*
- Define the reference architecture and MCP server build patterns that product teams across the organization follow when exposing their APIs as agent\-consumable tools.
- Partner closely with data engineers, product managers, and domain experts in insurance and property risk to ensure that AI systems produce outputs that are accurate, traceable, and operationally meaningful.
- Review implementations, conduct architecture design reviews, and hold the technical quality bar as the connector portfolio grows and more teams contribute.
- Produce architecture decision records, technical standards, and reference documentation that engineering teams across the organization rely on to make consistent, well\-reasoned decisions.
Job Qualifications:
Required Qualifications
- 7 to 10 years of software engineering or architecture experience, with a strong background in API platforms and distributed systems, and recent proven depth building LLM\-powered or agentic AI applications.
- Strong backend development background in Python, Java, or .NET, with an expert understanding of object\-oriented design and distributed microservices. As the AI orchestration ecosystem is heavily Python\-centric, candidates must be proficient in Python or demonstrably capable of transitioning into it for the agentic and tooling layers.
- Hands\-on experience with agentic AI frameworks such as LangChain, LlamaIndex, or AutoGen, with strong command of prompt engineering, context management, and tool integration.
- Solid foundation in API architecture and authentication patterns, including REST, OAuth 2\.0, JWT design, and API gateway technologies such as Apigee or Kong, with an understanding of how security is enforced across service boundaries.
- Working knowledge of Agent Experience (AX) design principles including context window constraints, prompt drift, tool description quality, and the failure modes that emerge when API schemas are ambiguous or inconsistent to an LLM.
- Familiarity with AI observability tooling and the operational differences between monitoring traditional software systems and monitoring non\-deterministic LLM systems.
- Experience with CI/CD practices and a working understanding of how to apply evaluation and testing frameworks to non\-deterministic AI systems.
- Clear, precise written communication with the ability to produce technical documentation that non\-specialist stakeholders can act on.
Preferred Qualifications
- Experience with MCP (Model Context Protocol), LiteLLM, or LLM gateway and proxy patterns.
- Familiarity with Snowflake, Databricks, or GCP.
- Hands\-on experience with containerized deployment environments including Docker, Kubernetes, and Terraform or equivalent IaC tooling.
- Strong understanding of AI infrastructure patterns, including Kubernetes GPU scheduling with taints, tolerations, and NVIDIA Device Plugins, Terraform for heterogeneous node pools, and containerization best practices for CUDA or ROCm (Radeon Open Ecosystems) environments.
- Background in regulated industries such as insurance, financial services, or healthcare, where data provenance, audit logging, and compliance\-oriented architecture are part of the design requirements.
- Experience with multi\-cloud architectures and cross\-cloud connectivity patterns.
- Exposure to developer experience tooling API documentation platforms, sandbox environments, or SDK design
Annual Pay Range:
134,400 \- 200,000 USD
Application Window:
This opportunity is expected to remain posted through the date identified below, subject to business needs.
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!
*Please note, Qualifications, locations and experience of the individual ultimately selected for the position may impact the final actual offered compensation, which may vary from the posted range*
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-$200K range is below 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 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($167K) sits 24% below the category median. Disclosed range: $134K to $200K.
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.
Cotality AI Hiring
Cotality has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Milwaukee, WI, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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