Interested in this AI/ML Engineer role at Brown & Brown Insurance?
Apply Now →Skills & Technologies
About This Role
Built on meritocracy, our unique company culture rewards self\-starters and those who are committed to doing what is best for our customers.
Brown \& Brown is seeking a Director, AI \& Security Development to lead one of the most strategic and forward\-looking pillars within our cybersecurity organization.
This leadership role will build and scale a dedicated function responsible for cybersecurity Agentic AI development, security tooling and automation engineering, data analytics and reporting enablement, while contributing to our secure development practices. This role reports directly to the Chief Information Security Officer and operates alongside leadership for Security Operations, Security Architecture, and Governance, Risk \& Compliance.
How You Will Contribute:
- Lead a core cybersecurity capability by defining the strategy, operating model, and service offerings for AI \& Security Development.
- Drive development of AI\-powered security agents to support detection, triage, investigation, and response.
- Build and scale automation and orchestration capabilities across enterprise security technologies.
- Enable security data integration, analytics, and reporting to support operational and executive decision\-making.
- Establish secure AI development practices and DevSecOps governance.
- Partner closely with Security Operations, Architecture, and GRC. Build and lead a high\-performing team.
- Translate technical capabilities into measurable business value and risk reduction.
Skills \& Experience to be Successful:
- 10\+ years of experience in cybersecurity, software engineering, or related technical disciplines, including 5\+ years in leadership roles.
- Proven experience building or scaling automation, AI/ML, or advanced analytics capabilities.
- Strong background in secure software development, cloud platforms (AWS, Azure, GCP), and API\-driven systems.
- Experience with security operations, detection engineering, or incident response workflows.
- Ability to operate at both strategic and hands\-on levels. Strong communication skills and executive presence.
- Experience with enterprise AI platforms or LLM\-based systems. Familiarity with AI risk management and governance frameworks. (preferred)
- Experience in financial services or other regulated environments. (preferred)
- Relevant certifications such as CISSP, CISM, or cloud/AI certifications. (preferred)
Pay Range
$210,000 \- $214,000 Annual*The pay range provided above is made in good faith and based on our lowest and highest annual salary or hourly* *rate paid for the role and takes into account years of experience required, geography, and/or budget for the role.*
Teammate Benefits \& Total Well\-Being
We go beyond standard benefits, focusing on the total well\-being of our teammates, including:
- *Health Benefits*: Medical/Rx, Dental, Vision, Life Insurance, Disability Insurance
- *Financial Benefits*: ESPP; 401k; Student Loan Assistance; Tuition Reimbursement
- *Mental Health \& Wellness*: Free Mental Health \& Enhanced Advocacy Services
- *Beyond Benefits*: Paid Time Off, Holidays, Preferred Partner Discounts and more.
*Not reflective of all benefits. Enrollment waiting periods or eligibility criteria may apply to certain benefits. Benefit details and offerings may vary for subsidiary entities or in specific geographic locations.*
The Power To Be Yourself
As an Equal Opportunity Employer, we are committed to fostering an inclusive environment comprised of people from all backgrounds, with a variety of experiences and perspectives, guided by our Diversity, Inclusion \& Belonging (DIB) motto, “The Power to Be Yourself”.
Salary Context
This $210K-$214K range is above 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 Brown & Brown 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 ($212K) sits 17% above the category median. Disclosed range: $210K to $214K.
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.
Brown & Brown Insurance AI Hiring
Brown & Brown Insurance has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $214K - $214K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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.