Interested in this AI/ML Engineer role at CNA Insurance?
Apply Now →Skills & Technologies
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.
A senior individual contributor role responsible for designing, building, and operationalizing end\-to\-end AI and machine learning solutions that accelerate CNA's migration to a modern cloud data lakehouse. The engineer works across structured and unstructured data domains — including documents, images, audio, and transactional records — to unlock analytical value through scalable pipelines, RAG architectures, vector databases, and knowledge graphs. This role may also provide guidance to others to support the building of complex technical capabilities.JOB DESCRIPTION:
Essential Duties \& Responsibilities
*Performs a combination of duties in accordance with departmental guidelines:*
- Design and build AI solutions that accelerate data migration from legacy systems to the cloud, ensuring scalability, reliability, and governance compliance.
- Design and implement scalable ingestion and transformation pipelines across structured (SQL, relational) and unstructured (documents, images, audio, email, call transcripts) data sources, applying OCR, NLP preprocessing, and document chunking strategies optimized for LLM consumption.
- Implement modern lakehouse patterns on Google Cloud Platform (GCP) — including data governance, cataloging, and lineage tracking — to ensure data is reliably discoverable, auditable, and fit for AI/ML workloads at scale.
- Design and implement vector databases, embedding pipelines, and knowledge graph structures that serve as the foundational retrieval layer for RAG and other AI applications.
- Productionize and operationalize AI solutions and advanced analytics in a DevOps/MLOps environment, including automated testing, monitoring, and rollback capabilities.
- Cultivate innovation by proactively proposing new ideas and identifying the right combination of tools and frameworks to turn business problems into analytics solutions.
- Researches, identifies and implements process improvements that address complex technology gaps. Builds strong knowledge of technology enablers.
*May perform additional duties as assigned.*
Reporting Relationship
Typically Director or above
Skills, Knowledge \& Abilities
- Deep expertise building scalable ingestion and transformation pipelines across structured and unstructured data sources; strong background migrating workloads from legacy systems to modern cloud platforms.
- Skilled in parsing and normalizing diverse content types — PDFs, emails, images, and call transcripts — using OCR, NLP preprocessing (tokenization, entity extraction, summarization), and document chunking strategies optimized for LLM consumption.
- Proven experience designing and implementing vector databases (e.g., Vertex AI Vector Search, Pinecone, pgvector), embedding pipelines, and knowledge graph structures that underpin RAG and semantic search applications
- Strong SQL and data analytical skills; experience building data marts and feature datasets for data science and ML applications.
- Strong coding fluency in Python; hands\-on experience with BigQuery, Claude Code, RAG architectures, LLMs, ADK, and prompt engineering techniques
- Expertise in building ML platforms and data pipelines at scale; familiarity with major ML algorithms, deep learning, NLP, information retrieval, and data mining techniques
- Experience with GCP services (Vertex AI, Dataflow, BigQuery, Cloud Run, Pub/Sub); comfort with distributed computing frameworks (Apache Spark, Dataproc) for large\-scale data processing.
- Solid experience managing diverse data sources including preprocessing, cleansing, and verifying data integrity to meet data science and ML requirements
- Demonstrated experience with machine learning, deep learning, information retrieval, NLP, or data mining — particularly applied to unstructured or semi\-structured data
- Hands\-on experience with vector databases, embedding models (e.g., text\-embedding\-gecko, OpenAI Ada, Cohere), and end\-to\-end RAG pipeline design
- Experience using Agile methods preferred.
- Strong communication and interpersonal skills and the ability to work effectively with peers and team members in a highly matrixed environment.
- Preferred experience with the insurance industry, its products and services.
- Experience in implementing big data processing technology. Apache Spark preferred.
Education \& Experience
- Bachelor's Degree in Computer Science, Engineering, Mathematics, Computational Statistics, Data Science, or a related technical field (or equivalent experience); Master's Degree preferred.
- Typically 7\+ years of experience in data engineering, Artificial Intelligence or Machine Learning.
- 2\+ years of coding proficiency in at least one programming language (Python, Java, SQL).
- Applicable certifications preferred (GCP, Data Engineering).
\#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 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 $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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.