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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.
The System Architect (SA) Director for AI Platforms Engineering serves as the technical owner for the enterprise AI platform which is the shared foundation powering all AI and GenAI products across the organization. This leader owns the platform's architecture, engineering standards, and delivery roadmap, translating strategic AI capabilities into reliable, scalable, and governed platform capabilities that accelerate every product team building on top of them.
Working in close partnership with Enterprise Architects, Product Management, and Release Train Engineers (RTEs), the SA Director ensures that platform investments are tightly aligned to business outcomes, compliance requirements, and engineering excellence. This role combines the strategic depth of a principal architect with the hands\-on leadership of a delivery\-focused engineering director.JOB DESCRIPTION:
Essential Duties \& Responsibilities
*Performs a combination of duties in accordance with departmental guidelines:*
- Own and continuously evolve the enterprise AI Platform reference architecture, encompassing all critical layers including model serving, orchestration engines, data and knowledge grounding pipelines, observability infrastructure, and ensuring the platform scales reliably to enterprise\-grade workloads and usage patterns.
- Define and enforce platform\-wide standards, reusable design patterns, and golden\-path templates that enable product and feature teams to build, deploy, and operate AI solutions safely, consistently, and with significantly reduced time\-to\-production.
- Drive end\-to\-end delivery of new platform capabilities — from initial technical discovery and architecture design through prototyping, hardening, and full production rollout while maintaining meaningful hands\-on involvement at critical technical milestones to ensure quality and coherence.
- Architect and operationalize the core platform service catalog, including LLM gateway and routing layers, prompt lifecycle management, agentic orchestration frameworks, Retrieval\-Augmented Generation (RAG) pipelines, vector stores, model registries, and rigorous automated evaluation infrastructure.
- Build and maintain robust CI/CD and AIOps pipelines specifically designed for AI systems, incorporating automated evaluation gates, model and data versioning controls, staged deployment promotion, and continuous cost and performance optimization guardrails.
- Architect enterprise\-grade multi\-agent and single\-agent workflow patterns for high\-value business use cases, establishing clear standards for orchestration design, state and memory management, tool and API integration, and safe autonomy controls including human\-in\-the\-loop approvals, permission scoping, and comprehensive audit trails.
- Design and implement knowledge grounding systems — spanning hybrid retrieval strategies, semantic reranking, ontology\-driven entity modeling, and knowledge graph integration — to measurably improve AI output accuracy, traceability, and readiness for regulatory audit.
- Embed responsible AI and compliance\-by\-design principles into every layer of the platform, covering data privacy protections, enterprise secrets management, granular access controls, output leakage prevention, and model risk governance practices aligned to enterprise and regulatory standards.
- Actively shape PI Planning by authoring well\-defined Enabler Epics and articulating architectural outcomes that anchor near\-term delivery and long\-horizon platform capability roadmaps, while contributing expert WSJF input to balance platform investment against feature team needs, risk reduction, and time\-to\-impact.
- Directly manage, mentor, and grow a high\-performing team of platform engineers, solution architects, and technical specialists — hiring hands\-on builders, coaching technical leadership skills, and sustaining a healthy innovation pipeline that continuously advances the organization's AI platform maturity.
*May perform additional duties as assigned.*
Skills, Knowledge \& Abilities
- Deep AI Platform and AIOps engineering expertise, including hands\-on experience designing, deploying, and operating shared AI platform capabilities such as model serving layers, LLM gateway and proxy services, prompt registries, vector databases, and automated evaluation harnesses at enterprise scale.
- Proven agentic system design capability, with hands\-on experience architecting multi\-agent and single\-agent workflow systems using orchestration frameworks such as Lang Graph, Google ADK — including tool and function calling patterns, state and memory persistence strategies, and robust safe autonomy controls.
- Applied GenAI depth spanning LLM solution architecture patterns, model selection and routing strategies, advanced prompt engineering techniques, fine\-tuning and RLHF tradeoffs, and production\-grade RAG and hybrid retrieval system design and optimization.
- Strong cloud\-native and distributed systems architecture skills, with deep GCP expertise across Vertex AI, Cloud Run, GKE, Pub/Sub, and BigQuery, and a solid command of API and service\-based design, event\-driven architecture, and high\-availability and fault\-tolerant system patterns.
- Knowledge grounding and semantic layer proficiency, including experience building canonical ontology and entity models, designing vector search and hybrid retrieval pipelines, integrating knowledge graphs, implementing reranking strategies, and establishing citation and traceability mechanisms that support compliance.
- Solid AIOps and platform reliability engineering experience, including CI/CD pipeline design for AI systems, automated evaluation and quality gates, model and dataset versioning, production monitoring and observability, reliability engineering practices, and systematic cost\-performance optimization.
- Practical responsible AI and security expertise, with demonstrated experience implementing enterprise AI governance frameworks, model risk management programs, PII and data privacy controls, audit and event logging, and compliance\-by\-design patterns suited to regulated industries.
- Strong SDLC and hands\-on engineering fundamentals, including Python proficiency, architectural and code review practices, comprehensive testing strategies for AI systems, technical debt management, refactoring discipline, and operational readiness standards.
- Scaled Agile (SAFe) leadership experience, including decomposing long\-horizon strategy into actionable Enabler Epics, shaping PI planning outcomes.
- Exceptional leadership and communication skills, with a demonstrated ability to influence senior stakeholders and cross\-functional teams, negotiate complex technology tradeoffs, mentor and develop engineers at all levels, and translate deep technical concepts into compelling narratives for non\-technical business audiences.
Education \& Experience
- Bachelor's degree in Computer Science, Software Engineering, Information Technology, or equivalent required; Master's degree in AI, Machine Learning, Data Science, or related discipline strongly preferred.
- 10\+ years in software engineering and technical delivery, with demonstrated ownership of large\-scale, distributed enterprise systems across the full SDLC from inception through production operations.
- 5\+ years in system or solution architecture, with a track record of producing reference architectures, design patterns, technical standards, and enterprise\-scale platform guardrails.
- 5\+ years of direct people leadership, including hiring, performance management, career development, and building high\-performing engineering and architecture teams.
- 5\+ years hands\-on designing, delivering, and operating AI/ML or GenAI platform capabilities in production, with measurable outcomes in quality, reliability, and developer adoption.
- Strong Python proficiency and deep practical GCP experience — Vertex AI, GCP Agent Builder, and Gemini — with the ability to engage credibly in hands\-on technical work alongside the engineering team.
- Prior experience in regulated industries (insurance, financial services, or healthcare) strongly preferred, given stringent governance, auditability, and model risk management requirements.
- Consulting or enterprise delivery background is a plus, bringing structured problem\-solving and stakeholder management
\#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. Director-level AI roles across all categories have a median of $247,800. 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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