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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 responsible for designing, building, and operationalizing AI-driven solutions that integrate seamlessly into enterprise systems. This role combines hands-on engineering, architecture, and governance, ensuring AI solutions are scalable, secure, compliant, and aligned with business objectives. JOB DESCRIPTION:
Essential Duties & Responsibilities
- Performs a combination of duties in accordance with departmental guidelines:
- Design and build enterprise AI applications using LLMs, prompt workflows, RAG, and agent-based patterns
- Evaluate model quality, latency, cost, safety, and business effectiveness
- Implement responsible AI controls, observability, and governance for production AI systems
- Partner with architects, product owners, and business teams to identify and scale high-value AI use cases
- Convert prototypes into production-ready, reliable applications
- Enable automation of repeatable business processes using AI
- Develop APIs and microservices to expose AI capabilities
- Integrate AI solutions with enterprise platforms (claims, document systems, data platforms)
- Ensure scalability, resiliency, and cost optimization
- Implement test automation (unit, integration, end-to-end)
- Apply secure coding practices (OAuth, JWT, secrets management)
- Mentor engineers and guide AI best practices
- Collaborate with business units, IT, and vendors
- Evaluate emerging AI technologies and recommend solutions
May perform additional duties as assigned.
Reporting Relationship
- Typically, Director or above
Skills, Knowledge & Abilities
- Strong programming skills in Java, Python, TypeScript, FastAPI, Spring Boot for APIs and services.
- Experience using AI-enabled development tools such as GitHub Copilot, Claude Code, Cursor, and MCPs.
- Experience with RAG (Retrieval-Augmented Generation) pipeline architecture and development.
- Experience with agentic system design for multi-step automation (single-agent and multi-agent), including orchestration, state and memory patterns, tool integration, and safe autonomy controls (permissions, approvals, and auditability).
- Experience with Google Cloud Platform (GCP), including services such as GKE, Cloud Storage, PostgreSQL, Pub/Sub, Secret Manager, BigQuery, and Vertex AI.
- Advanced knowledge of designing and building integration platforms, APIs, and web services.
- Experience with CI/CD automation using tools such as Docker, GKE, GitHub, and GitHub Actions.
- Strong analytical, problem-solving, and communication skills
- Experience with SQL/NoSQL data stores (modeling and query optimization)
- Automated testing experience (unit and integration)
- Working knowledge of React and front-end fundamentals (components, state, and API integration)
- Strong understanding of SRE and observability fundamentals (logs, metrics, traces) and how to use them to diagnose distributed system behavior
- In-depth knowledge of the system development life cycle and of system and application development technology alternatives.
- Experience working in high-performing Agile teams
- Excellent communication and interpersonal skills, with the ability to work effectively with peers, IT management and staff, and internal and external business partners and clients.
- Preferred insurance industry knowledge.
Education & Experience
- Bachelor’s degree in computer science or related discipline, or equivalent work experience.
- Typically, a minimum of 7 years of systems analysis and application development experience, including 3+ years of experience building AI solutions.
Applicable certifications preferred. *
LI-Hybrid
I n 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 Software Engineer roles in our dataset (median: $194K across 269 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 11,520 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At CNA Insurance, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,688 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($106K) sits 52% below the category median. Disclosed range: $72K to $141K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
CNA Insurance AI Hiring
CNA Insurance has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Chicago, IL, US, New York, NY. Compensation range: $141K - $141K.
Location Context
AI roles in Chicago pay a median of $193,440 across 450 tracked positions. That's 9% below the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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