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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 providing the highest level of technical leadership in the design, development, and scaling of CNA's AI\-native agentic engineering platform. This role operates at the intersection of AI systems engineering, developer experience, and software delivery — building the foundational platform capabilities that enable the broad engineering organization to build, ship, and run high\-quality, secure AI\-native systems at the speed of AI. The focus is on designing and delivering agentic workflows, AI\-augmented CI/CD pipelines, reusable skills and agent frameworks, and quality/security guardrails that make AI\-accelerated delivery safe and scalable across the enterprise.JOB DESCRIPTION:
Essential Duties \& Responsibilities
*Performs a combination of dutiesin accordance withdepartmental guidelines:*
- Acts as one of principal engineers for CNA's AI\-native engineering platform, designing the end\-to\-end system spanning agentic coding workflows, skills and agent marketplaces, AI\-augmented CI/CD pipelines, automated quality gates, and rapid environment provisioning. Leads integration of AI tooling (Claude Code, Cursor, GitHub Copilot) into the software delivery lifecycle, ensuring these capabilities compose into a coherent, governed platform.
- Designs and builds the agentic infrastructure layer — including multi\-agent orchestration patterns, sub\-agent frameworks, skill authoring standards, and context engineering best practices — that enables engineering teams to operate at AI\-native speed without sacrificing architectural integrity or security posture.
- Provides expert technical consultation to engineering leadership, portfolio teams, and architecture on how to adopt AI\-native development practices, evaluate AI\-generated code quality, and integrate agentic tooling into existing workflows. Advises on trade\-offs between speed and quality, human\-in\-the\-loop requirements, and appropriate levels of AI autonomy for different risk profiles (e.g., Sox\-classified systems vs. rapid prototyping).
- Leads the technical strategy and implementation for engineering metrics platform. Consult with senior tech leadership stakeholders to provide insights in data\-driven decision making.
- Acts as the senior technical resource mentoring engineers across the organization in AI\-native engineering practices — including agentic coding patterns, context engineering, prompt\-to\-code workflows, and AI\-assisted testing — raising the floor of capability so teams become self\-sustaining without ongoing coaching dependency.
- Researches, evaluates, and recommends AI engineering tools, frameworks, and infrastructure (e.g., eval platforms, agent orchestration systems, environment provisioning automation) aligned with CNA's strategic direction. Leads build\-vs\-buy analysis for platform capabilities such as CI/CD tooling, sandbox provisioning, and LLM evaluation infrastructure.
- Partners closely with Architecture, Security, Cloud Engineering, and Data teams to ensure the AI engineering platform integrates with enterprise infrastructure (GCP/GKE, GitHub, JFrog Artifactory), meets regulatory and compliance requirements (AI model tracking, Sox controls), and scales to support hundreds of engineers and AI pod teams across all portfolios.
*May performadditionalduties as assigned.*
Reporting Relationship
Typically Director or above
Skills, Knowledge \& Abilities
- Expert knowledge in AI\-native software engineering practices including agentic coding workflows (Claude Code, Cursor, GitHub Copilot), prompt and context engineering, multi\-agent orchestration, MCP protocol, and skill/agent authoring patterns.
- Deep understanding of the modern software delivery lifecycle with specific expertise in how AI transforms each phase — from AI\-assisted requirements and design through agentic code generation, automated testing, AI\-augmented code review, and continuous deployment.
- Proficient in building and operating CI/CD platforms (GitHub Actions or equivalent), infrastructure\-as\-code (Terraform), container orchestration (GKE/Kubernetes), and cloud platforms (GCP), with the ability to design pipelines that enforce quality and security gates without creating delivery bottlenecks.
- Strong knowledge of application security engineering including supply chain security, artifact management and curation, static/dynamic analysis, secret management, and the specific attack vectors introduced by AI\-generated code (dependency hallucination, model drift, prompt injection).
- Demonstrated ability to design developer platforms and tooling that serve hundreds of engineers at varying skill levels — balancing power\-user capability with guardrails that prevent misuse and maintain code quality at scale.
- Proven ability to evaluate and integrate emerging AI technologies rapidly, with the judgment to distinguish between hype and production\-ready capability. Comfortable operating in a fast\-moving domain where the tooling landscape changes weekly.
- Excellent communication skills with the ability to translate complex AI engineering concepts for both technical and non\-technical audiences. Able to influence engineering culture and drive adoption of new practices across a large, diverse organization including internal teams and managed service providers.
- Strong analytical and problem\-solving skills with an outcomes\-oriented mindset — focused on measurable improvements in delivery speed, code quality, and engineering productivity rather than tooling adoption metrics.
Education \& Experience
- Bachelor's Degree with Master's preferred in Computer Science, AI/ML, or related discipline, or equivalent work experience.
- Minimum of 9 years of solid, diverse work experience in software engineering with a minimum of 6 years in application development, including significant recent experience (2\+ years) building or operating AI\-augmented development tools, agentic systems, or developer platforms.
- Demonstrated hands\-on experience with LLM\-based engineering tools (Claude Code, Cursor, GitHub Copilot, or equivalent) in production engineering workflows, not just experimental use.
- Experience in designing and scaling inner\-source or platform engineering programs across large engineering organizations preferred.
- Applicable certifications in cloud platforms (GCP, AWS), AI/ML, or security 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* *$97,000 to $189,000* *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 utilizes AI\-enabled technology during the recruiting process. For more information, please visit our careers page.
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 $97K-$189K range is below the median 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 ($143K) sits 21% below the category median. Disclosed range: $97K to $189K.
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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