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About This Role
You have a clear vision of where your career can go. And we have the leadership to help you get there. At CNA, we strive to create a culture in which people know they matter and are part of something important, ensuring the abilities of all employees are used to their fullest potential.
Individual contributor with experience of AI native platform development and responsible for platform adoption across a CNA engineering community.
This role requires expertise in systems analysis and design, application development and/or integration, and testing of complex systems applications to support CNA engineering team needs. Providing guidance to others informally and formally leads teams on a project and/or program. The focus of this position will be leading, architecting, designing, developing, or integrating AI based engineering platforms applications in existing development and testing workflows. Preferred experience with background as SDET or deep experience with Testing tools like Playwright or Cypress etc.JOB DESCRIPTION:
Essential Duties \& Responsibilities
*Performs a combination of duties in accordance with departmental guidelines:*
- Lead the architecture, design, development, and integration of AI\-based engineering platforms, applications, agents, and skills that support software delivery and improve developer productivity.
- Drive adoption of AI\-enabled development platforms across the engineering community by embedding capabilities by doing road show, COPs and Demo sessions.
- Provide technical leadership and consultation to engineering teams on AI solution design, application integration, prompt and workflow patterns, and secure, scalable implementation approaches.
- Design and implement robust testing and validation approaches for AI\-enabled applications and platforms, including automated testing, quality assurance, and integration into CI/CD pipelines.
- Research, evaluate, and recommend AI tools, frameworks, models, and platform capabilities to determine the most effective and cost\-efficient solutions for business and engineering needs.
- Collaborate with engineering, infrastructure, security, testing, and external partners to align requirements, integrate solutions, and ensure successful delivery of AI platform capabilities.
- Contribute to AI engineering strategy, architecture direction, governance, and best practices, including reusable patterns for platform development, integration, observability, and supportability.
- Support deployment, monitoring, issue resolution, and ongoing enhancement of AI\-based applications and platforms in test and production environments to ensure reliability, performance, and continuous improvement.
*May perform additional duties as assigned.*
Reporting Relationship
Typically Director or above
Skills, Knowledge \& Abilities
- Solid knowledge of use of AI native development tools e.g. Cursor, GitHub Copilot, Claude Code, and MCPs.
- Solid technical skills for developing AI based platforms/apps/agents/skills etc.
- Experience with development of EVALs and Observability platforms for AI native solutions
- Experienced in AI model development and app design, specifically Vertex AI.
- Experience with RAG (Retrieval\-Augmented Generation) pipeline architecture and development
- Solid technical knowledge of high\-level programming languages like Python and/or Typescript, Java, JavaScript,
- Solid technical knowledge of high\-level programming languages, databases, interfaces, and familiarity with application program development alternatives.
- Advanced knowledge in designing and building Integration platforms, APIs and Webservices.
- Working knowledge of different versions of Dev tools like VS Code or JetBrains IDEs; Git and GitHub; npm/yarn, pip, and/or maven/gradle; docker – 5\+ years of experience.
- Good experience in development of AI native system development life cycle, and application program development technological alternatives.
- Proven understanding of state\-of\-the\-art application development support software packages,
- proficiency in at least one higher level programming language.
- Proven solid analytical and problem\-solving skills.
- Excellent communications and interpersonal skills and the ability to work effectively with peers, IT management and staff, and internal/external business partners/clients.
- Ability to manage projects, lead teams, and mentor individuals.
- Preferred insurance industry knowledge.
- Preferred experience with React or other front\-end frameworks.
- Preferred experience as an SDET or deep knowledge of test automation tools like Playwright, Cypress
Education \& Experience
- Bachelor's degree in Computer Science, or related discipline, or equivalent work experience.
- Typically a minimum of seven years of systems analysis and application program development experience.
- Some previous project leadership experience.
- Applicable certifications preferred.
\#LI\-KJ1 \#LI\-HYBRID
*In certain jurisdictions, CNA is legally required to include a reasonable estimate of the compensation for this role. In* *District of Columbia,California, Colorado, Connecticut,* *Illinois*, *Maryland,* *Massachusetts*, *New York and Washington,* *the national base pay range for this job level is* *$72,000 to $141,000* *annually. Salary* *determinations are based on various factors, including but not limited to, relevant work experience, skills, certifications and location. CNA offers a comprehensive and competitive benefits package to help our employees – and their family members – achieve their physical, financial, emotional and social wellbeing goals. For a detailed look at CNA’s benefits, please visit* *cnabenefits.com**.*
CNA is committed to providing reasonable accommodations to qualified individuals with disabilities in the recruitment process. To request an accommodation, please contact [email protected].
Salary Context
This $72K-$141K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At CNA Insurance, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($106K) sits 41% below the category median. Disclosed range: $72K to $141K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
CNA Insurance AI Hiring
CNA Insurance has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Lake Mary, FL, US, Chicago, IL, US. Compensation range: $141K - $189K.
Location Context
AI roles in Chicago pay a median of $201,225 across 312 tracked positions.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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