Interested in this AI/ML Engineer role at CCC Intelligent Solutions?
Apply Now →About This Role
Salary range is:
$217,236\.65 \- $300,000\.00
This position is equity and bonus and/or commission eligible.
CCC Intelligent Solutions Inc. (CCC) is a leading cloud platform for the multi\-trillion\-dollar insurance economy, creating intelligent experiences for insurers, repairers, automakers, part suppliers, and more. At CCC, we’re making life just work by empowering more than 35,000 businesses with industry\-leading technology to get drivers back on the road and to health quickly and seamlessly. We’re pushing boundaries with innovative AI solutions that simplify and enhance the claims and repair journey. Through purposeful innovation and the strength of its connections, CCC technologies empower the people and industry relied upon to keep lives moving forward when it matters most. Learn more about CCC at www.cccis.com.
The Role
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The Chief Architect, AI \& Platform will be responsible for setting CCC’s enterprise\-wide technical architecture vision and ensuring that our platforms, products, data systems, AI capabilities, and engineering practices scale together.
This leader will partner closely with Product, Engineering, Data Science, Security, Infrastructure, Client Success, and executive leadership to translate business strategy into a durable technical architecture. The ideal candidate is a hands\-on strategic technologist who can move fluidly from board\-level technology strategy to deep technical design conversations with principal engineers, AI researchers, platform teams, and product leaders.
This is a role for someone who understands modern SaaS platforms, AI\-native product development, event\-driven systems, high\-scale data architecture, enterprise integration, and the operational discipline required for trusted automation in regulated, mission\-critical environments.Key Responsibilities:
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Technology Vision and Architecture Strategy
You will define and evolve CCC’s long\-term architecture strategy across multiple cloud platforms, product domains, data platforms, APIs, integration patterns, AI/ML systems, and developer experience. You will ensure that architectural choices support CCC’s growth, customer trust, speed of innovation, operational resilience, and ability to scale AI\-enabled workflows across the insurance economy.
You will establish clear architectural principles and decision frameworks that help teams move faster while avoiding unnecessary fragmentation, duplication, and technical debt.
AI\-Native Platform Architecture
You will guide CCC’s transition from software that uses AI to a platform that is increasingly AI\-native. This includes architecture for predictive AI, computer vision, generative AI, agentic workflows, human\-in\-the\-loop decisioning, model orchestration, evaluation frameworks, AI observability, model governance, and safe automation.
You will help ensure CCC can deploy AI responsibly across high\-consequence workflows such as damage assessment, estimating, claims routing, injury prediction, subrogation, total loss prediction, and customer communications.
Data Architecture
You will lead the architecture for CCC’s data foundation, including data products, event streams, metadata, lineage, governance, data contracts, lakehouse/warehouse patterns, real\-time decisioning, and customer\-facing analytics. Your goal will be to help CCC turn its ecosystem data advantage into reusable platform capabilities while maintaining privacy, security, accuracy, and customer trust.
You will partner with data science and product teams to make CCC’s proprietary data, workflow context, and ecosystem signals easier to use in AI models, product experiences, partner integrations, and internal decision systems.
Platform Modernization and Scalability
You will guide the evolution of CCC’s multi\-tenant SaaS architecture, cloud infrastructure, service boundaries, APIs, event\-driven systems, integration patterns, and shared platform capabilities. You will help engineering teams make smart decisions about build vs. buy, modularity, service ownership, resilience, latency, cost efficiency, and operational maturity.
The goal is not architecture purity. The goal is a platform that lets CCC innovate faster, integrate more easily, serve enterprise customers reliably, and scale AI\-powered workflows across many product lines and customer segments.
Architecture Operating Model
You will build an architecture function that enables teams rather than slows them down. This includes creating lightweight governance, architecture review practices, reusable patterns, reference implementations, decision records, and technical roadmaps.
You will mentor principal engineers and architects, raise the technical bar across engineering, and help create a culture where architecture is practical, measurable, and connected to business outcomes.
Security, Trust, and Responsible AI
You will partner with Security, Legal, Compliance, and Product to ensure CCC’s AI and platform architecture is secure, explainable where needed, auditable, privacy\-aware, and resilient. This includes modern AI risk controls such as prompt\-injection defenses, secure model supply chains, output validation, data leakage prevention, model monitoring, and human oversight.
What Success Looks Like
In the first 6 months, this leader will have created a clear architecture map of CCC’s current platform landscape, identified the highest\-leverage modernization opportunities, established practical architecture principles, and built trust with senior engineering, product, AI, security, and executive stakeholders.
In the first 12 months, this leader will have shaped a multi\-year AI and platform architecture roadmap, reduced architectural fragmentation in priority areas, improved reuse of core platform and data capabilities, strengthened AI governance and evaluation practices, and accelerated delivery of AI\-enabled product capabilities.
Longer term, success means CCC has a more modular, scalable, AI\-native platform architecture that strengthens its ecosystem advantage, improves engineering velocity, supports trusted automation, and creates more leverage from CCC’s data, workflows, and network.
Requirements:
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- 15\+ years of technology experience, including significant leadership experience in SaaS, platform architecture, enterprise software, data\-intensive systems, or AI\-enabled products.
- Proven experience architecting large\-scale, cloud\-native, multi\-tenant platforms used by enterprise customers.
- Deep understanding of distributed systems, event\-driven architecture, APIs, microservices or modular monolith patterns, cloud infrastructure, security, observability, and reliability engineering.
- Strong experience with data architecture, including real\-time data, analytics platforms, data governance, metadata, data quality, and data products.
- Practical understanding of AI/ML systems, including model lifecycle management, MLOps/LLMOps, generative AI patterns, evaluation, monitoring, responsible AI, and human\-in\-the\-loop workflows.
- Experience influencing across large engineering organizations without relying only on formal authority.
- Ability to communicate architecture tradeoffs clearly to executives, product leaders, engineers, customers, and non\-technical stakeholders.
- A track record of balancing innovation with reliability, security, customer trust, and business outcomes.
Preferred Qualifications
- Experience in insurance, automotive, collision repair, fintech, healthcare, logistics, or another complex workflow\-heavy industry.
- Experience building platforms that support marketplaces, partner ecosystems, or multi\-party networks.
- Experience with computer vision, claims automation, document intelligence, decision automation, or workflow orchestration.
- Experience modernizing legacy platforms while continuing to support large enterprise customers.
- Familiarity with AI governance frameworks, model risk management, privacy requirements, secure software development, and regulated\-industry expectations.
- Experience working with public\-company technology, security, and operating expectations.
Interview Policy \& Privacy Notice:
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A video interview is required for this position. Video interviews are transcribed. Transcriptions are retained and may be reviewed by CCC and our recruiters. Candidates are not permitted to use generative AI or automated assistance during the interviews unless explicitly allowed by the interview team for a specific exercise. Our Job Applicant Privacy Notice is available HERE.
About CCC's Commitment to Employees:
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CCC Intelligent Solutions understands that our employees play an integral role in our vision to shape a world where life just works. Our team is defined by our values of Integrity, Customer\-Focus, Innovation, Inclusion \& Diversity, Tenacity, and Connection. Through diverse perspectives, purposeful innovation, and the strength of connections, our technologies empower the people and industry relied upon to keep lives moving forward when it matters most.
At CCC, together everyone can thrive as we innovate and collaborate, creating employee experiences that just work. We are committed to providing opportunities for our people to make real\-life impacts, advance in their careers, and contribute to CCC’s success.
CCC offers competitive compensation and benefits to support you and your families, including:
- 401K Match
- Paid time off
- Annual Incentive Plan Performance Bonus
- Comprehensive health insurance
- Adoption Assistance
- Tuition Reimbursement
- Wellness Programs
- Stock Purchase Plan options
- Employee Resource Groups
For more information about our benefits, please check out our careers site.
Here, you belong. You are seen, valued, and respected. We celebrate you for who you are and all you bring. Every voice is heard and is important to our success. You can hear what employees have to say about our culture here
If you require reasonable accommodation to complete a job application, please contact (800\) 621\-8070\.
Salary Context
This $217K-$300K range is above the 75th percentile 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 CCC Intelligent Solutions, 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 $214,900 based on 6,420 positions with disclosed compensation. This role's midpoint ($258K) sits 20% above the category median. Disclosed range: $217K to $300K.
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.
CCC Intelligent Solutions AI Hiring
CCC Intelligent Solutions has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $89K - $300K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national 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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