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Job Title
AI Workflow and Systems Design Lead - RemoteRequisition Number
R8029 AI Workflow and Systems Design Lead - Remote (Open)Location
Arizona - Home TeleworkersAdditional Locations
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CSAA Insurance Group (CSAA IG), a AAA insurer, is one of the leading personal lines property and casualty insurance groups in the United States. Here, every employee shapes our mission. We build innovative, human-centered solutions that help AAA members prevent, prepare for, and recover from life's uncertainties. You will join a collaborative, inclusive culture where your strengths have room to grow and your ideas can drive real impact. Step into a role where you can contribute to our shared success through meaningful work.
We are actively hiring for an AI Workflow and Systems Design Lead - Remote
Your Role: The AI Workflow & Systems Designer is a senior individual contributor who partners across business and technology to redesign complex workflows and create AI-ready systems of work. The role leads discovery, defines how people and AI work together, translates business needs into implementation-ready designs, and helps build repeatable Enterprise Systems Design practices.
Your Work:
Lead AI Workflow Reinvention and Systems-of-Work Design
- Partner directly with business leaders and multi-functional teams to understand strategic goals, pain points, customer or employee needs, and opportunities for workflow reinvention.
- Lead current-state assessment and lead future-state design for sophisticated workflows spanning multiple teams, applications, channels, data sources, decisions, and controls.
- Design end-to-end systems of work that connect front-stage experiences with the backstage people, processes, technology, data, decisions, controls, AI agents, and support capabilities required to deliver the outcome.
- Move teams beyond application-level optimization by identifying how the complete outcome must work across organizational and system boundaries.
- Facilitate workshops and working sessions that move multi-functional teams from ambiguity to aligned future-state decisions.
- Translate sophisticated workflows into clear, visual, implementation-ready design artifacts.
Design Human + AI Ways of Working
- Identify where AI can create meaningful value within a workflow through automation, augmentation, recommendations, decision support, orchestration, or autonomous action.
- Decompose workflows into the elements needed for AI design, including tasks, decisions, required context, data and knowledge, tools and actions, business rules and controls, and exceptions and critical issue paths.
- Define the appropriate division of work between employees, customers, partners, AI agents, automation, and traditional systems.
- Design human-in-the-loop approaches that preserve appropriate judgment, accountability, oversight, and trust.
- Define intervention triggers, confidence thresholds, approval paths, critical issue logic, exception handling, overrides, feedback loops, and explain-ability requirements.
- Ensure AI-enabled workflow designs reflect both business value and operational realities rather than applying AI for its own sake.
Create Implementation-Ready Systems Design Artifacts
- Lead the development of systems design outputs that make sophisticated work visible, align multi-functional teams, and enable confident downstream decisions.
- Translate business outcomes and future-state concepts into clear, actionable representations of how work should operate across people, AI, processes, decisions, data, controls, and technology.
- Surface key cross-system requirements, dependencies, ownership questions, handoffs, risks, and unresolved decisions early enough to influence architecture and delivery.
- Clarify human and AI roles, decision points, exceptions, controls, and opportunities for automation or augmentation where relevant.
- Document future-state workflows, operating-model implications, and reusable patterns at the level of detail appropriate for the engagement.
- Adapt the format and depth of work to the complexity, maturity, and needs of each initiative rather than applying a one-size-fits-all approach.
Connect Business Intent to Technology Delivery
- Serve as a connective design partner between business goals and downstream technology implementation.
- Work closely with business divisions, BRMs, Architecture, Data, Engineering, and Governance teams during discovery and design.
- Surface cross-system requirements, dependencies, design choices, and unresolved decisions early enough to influence architecture and delivery.
- Translate business and experience needs into clear system-of-work requirements without prescribing technical architecture that belongs to Architecture or Engineering.
- Participate in feasibility discussions to ensure future-state designs are realistic, executable, and aligned with enterprise standards.
- Support implementation teams in understanding the design intent as work moves into build and delivery.
- Participate in design-assurance checkpoints when appropriate to ensure the implemented solution remains consistent with the intended system of work.
Design for Reuse, Not Just the Current Project
- Begin each engagement by identifying relevant existing enterprise patterns, components, services, agent skills, and other reusable capabilities.
- Look across projects for recurring workflows, decisions, interactions, controls, and human-AI patterns that should be solved once and reused.
- At the end of engagements, ensure useful patterns, methods, requirements, and lessons are collected and contributed back into the enterprise reusable asset ecosystem.
- Help shift teams from project-by-project invention toward design and reuse by default.
- Balance enterprise consistency with appropriate flexibility for different business domains and platforms.
Partner Closely with Business Leaders
- Act as a trusted design and transformation partner to business leaders working on strategic modernization, workflow reinvention, and AI initiatives.
- Help leaders articulate the desired business outcome before jumping to a technology solution.
- Translate business pain points into opportunities for workflow, operating-model, experience, and AI redesign.
- Facilitate tradeoffs across experience, operational efficiency, risk, technical feasibility, and value.
- Help teams visualize and understand future ways of working before investment and implementation decisions are finalized.
- Communicate sophisticated systems-design concepts in simple, business-friendly language.
- Build confidence that Enterprise Systems Design strengthens business ownership rather than taking it away.
Help Build the Enterprise Systems Design Practice
- Partner with the Senior Director to develop and refine CSAA's Enterprise Systems Design methods, standards, templates, and quality bar.
- Help establish repeatable approaches for systems-of-work design, workflow reinvention, human-AI design, and AI workflow decomposition.
- Contribute examples, templates, case studies, and reusable methods based on real project engagements.
Measure the Impact of Systems Design
- Partner with project teams to define success measures at the beginning of major engagements.
- Help establish baseline measures where practical.
- Gather qualitative and quantitative lessons that can improve future engagements.
- Help demonstrates the value of workflow reinvention and Systems Design through clear case studies and executive storytelling.
Required Experience, Education and Skills
- Bachelor's degree or equivalent practical experience.
- 10+ years of dynamic experience in systems design, service design, workflow/process transformation, design strategy, digital transformation, product/service innovation, management consulting, or product management.
- Deep expertise in systems thinking, service design, and workflow design, with demonstrated experience translating sophisticated business problems into coherent future-state systems.
- Demonstrated experience leading sophisticated multi-functional engagements involving multiple business units, systems, collaborators, or technology platforms.
- Solid ability to move between front-stage and backstage systems
- Solid ability to create and communicate design artifacts such as systems-of-work blueprints, service blueprints, workflow maps, dependency maps, role and handoff models, decision models, and operating-model designs.
- Strong AI fluency, including practical knowledge of generative AI, agents, automation, retrieval, decision support, and AI-enabled workflow patterns.
- Solid understanding of human-AI interaction and human-in-the-loop design.
- Ability to decompose sophisticated workflows into tasks, decisions, context, data, knowledge, tools, controls, and exceptions.
- Strong business insight and ability to connect design decisions to measurable business value.
- Experience facilitating senior-level and multi-functional workshops where participants have differing objectives or ownership.
- Strong visual communication and storytelling skills; able to simplify sophisticated systems into clear, compelling representations.
- High comfort with ambiguity and initiatives where no single function leads the entire outcome.
- Demonstrated ability to influence without formal authority and move groups toward decisions and commitments.
- Ability to work independently on high-profile engagements while collaborating effectively within a small, senior team.
What would make us excited about you?
- Master's degree in Design Strategy, Human-Computer Interaction, Service Design, Business, Organizational Design, Technology, or a related discipline.
- Relevant training or certification in Systems Thinking, Service Design, Human-Centered Design, Lean Six Sigma, Process Design, Change Management, or AI-related disciplines. Experience in insurance, financial services, or another regulated industry.
- Experience redesigning workflows specifically for AI, intelligent automation, or agentic systems.
- Experience in service design, business-process transformation, or operating-model redesign.
- Experience working with enterprise architecture and engineering organizations on cross-system initiatives.
- Experience identifying and creating reusable enterprise patterns, methods, components, or services.
- Familiarity with design systems and design-to-code practices.
- Experience applying human-centered design, Lean, Lean Six Sigma, or related transformation methodologies.
- Experience with prototyping, experimentation, pilot design, or testing future-state workflows before implementation.
- Experience coaching other designers or transformation practitioners.
- Actively shapes our company culture (e.g., supporting employee resource groups, mentoring employees, volunteering, joining cross-functional projects)
- Champions our cultural norms (e.g., willing to have cameras when it matters: helping onboard new team members, building relationships, etc.)
- Demonstrates a company ownership attitude, thinking beyond boundaries of their own area
- Travels as needed for role, including divisional / team meetings and other in-person meetings
- Fulfills business needs, which may include investing extra time, helping other teams, etc
Please note we are hiring for this role remote anywhere in the United States with the following exceptions: Hawaii and Alaska.
Why Choose a Career at CSAA IG?
At CSAA IG, we are a mission-driven organization proudly committed to empowering our members, our employees, and our communities to thrive.
Recognition: We offer a total compensation package, annual bonus eligibility for most roles, 401(k) with a company match, and so much more! Read more about what we offer and what it is like to be a part of our dynamic team at https://careers.csaainsurance.aaa.com/us/en/benefits.
Career Growth: We believe in growth for everyone. Here at CSAA IG, leaders and mentors partner with employees to align interests, unlock development opportunities, and support long‑term success.
Flexible Workplace: We embrace a remote-first culture through our Flexible Workplace. Most employees hold Home-Flex roles, working primarily from home, often with the flexibility to work from various locations including CSAA offices. Our flexible workplace empowers you to balance remote work with intentional in‑person moments that deepen connection and collaboration.
Inclusion and Belonging: An inclusive and welcoming workplace is the cornerstone of our success. By fostering an environment where people feel valued and heard, we deepen our ability to understand and meet the unique needs of our members. This strengthens innovation and enhances our products and services, giving us a competitive edge in the market.
Sustainability: As climate change leads to more frequent and severe weather events, we are taking bold action to build more resilient communities and reduce our environmental impact. Submit your application to be considered. We communicate via email, so check your inbox and/or your spam folder to ensure you don’t miss important updates from us.
CSAA is committed to providing reasonable accommodations to qualified applicants and employees with disabilities or other limitations. If you would like to request an accommodation to participate in the job application or interview process, please contact [email protected]
If you apply and are selected to continue in the recruiting process, we will schedule a preliminary call with you to discuss the role and will disclose during that call the available salary/hourly rate range based on your location. Factors used to determine the actual salary offered may include location, experience, or education.
CSAA does not provide visa sponsorship for this role. Applicants must have authorization to work indefinitely in the US. Please do not apply for this role if at any time (now or in the future) you will need immigration support (i.e., H-1B, TN, STEM OPT Training Plans, etc.).
CSAA Insurance Group is an equal opportunity employer.
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The national average salary range for this position is $144,450.00-$160,500.00. However, we have a location-based compensation structure. Our salary ranges vary and are calculated based on work location. The starting pay range for this position across all the states we hire in is $144,450.00-$192,550.00. This role also includes an opportunity for a company-wide annual discretionary bonus, through our Annual Incentive Plan (AIP), of up to 12% of eligible pay.Colorado Specific Job Range :$144,450.00 - $176,600.00 This job posting will be unposted on Fri, 23 Oct 2026.
Salary Context
This $144K-$192K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CSAA Insurance Group, a AAA Insurer, 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 in Demand for This Role
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. This role's midpoint ($168K) sits 21% below the category median. Disclosed range: $144K to $192K.
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.
CSAA Insurance Group, a AAA Insurer AI Hiring
CSAA Insurance Group, a AAA Insurer has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $192K - $192K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 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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