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About This Role
About Us
Integrity is an omnichannel insurtech company innovating insurance with a singular purpose: making insurance simpler and more human, so everyone can plan for the good days ahead! With billions in funding from HGGC, Harvest Partner, SilverLake, we leverage techniques that include predictive modeling, custom dynamic dashboards, next- best-action and behavior triggers, as well as other cutting-edge methods like natural language processing (NLP) to inform decision-making and streamline processes.
Integrity has experienced significant growth in the past three years, increasing earnings more than 800%. We are an employee owned company, and are also incredibly proud of our women in leadership, from our C-Suite executives to our managing partners and more (women also make up 63% of our workforce!) We recognize the importance of having equal representation throughout our organization — and that starts at the top!
Job Summary
Join a dynamic and fast-paced environment where technology drives innovation in the insurance industry. As an omnichannel Insurtech company, we believe work should be meaningful, impactful, and enjoyable. You’ll be working on one of the most exciting data and technology opportunities in the country while helping to change lives, including your own.
The Senior Director of AI & ML is a senior leadership role responsible for defining and executing the organization’s enterprise-wide Artificial Intelligence (AI) strategy. This role will oversee the development, integration, and scaling of AI, machine learning (ML), GenAI initiatives to drive operational efficiency, business innovation, and competitive advantage across Integrity. The ideal candidate combines deep technical expertise, strong executive presence with strategic acumen, and the ability to collaborate across departments to ensure AI initiatives are aligned with business goals.
Responsibilities
Strategic Leadership: ·
- Define and lead the organization's enterprise AI strategy, aligned with broader digital transformation objectives.
- Architect and champion, a forward-thinking AI vision that anticipates industry disruption.
- Identify, prioritize, and champion high-impact AI use cases across business functions (technology, sales, distribution, operations, finance, marketing, risk).
- Drive AI adoption and digital innovation, transforming data into actionable insights and business outcomes.
- Serve as a trusted advisor to the C-suite, translating complex AI concepts into actionable business strategies.
- Regularly present at board meetings and industry forums, shaping the company’s reputation as an innovator in AI.
Program Oversight & Execution: ·
- Oversee a portfolio of AI/ML initiatives, from proof-of-concept to production at scale.
- Ensure proper governance, compliance, risk management, and ethical AI practices.
- Establish KPIs and success metrics to monitor the effectiveness and ROI of AI initiatives.
- Manage vendor relationships, including cloud providers, AI platforms, and consulting partners.
Architecture, Data & Technology: ·
- Partner with Data Engineering, IT, and Architecture teams to ensure scalable AI infrastructure, data pipelines, and model deployment frameworks.
- Champion the adoption of MLOps, responsible AI principles, and reusable AI assets and platforms.
- Keep abreast of emerging AI trends and technologies and evaluate their applicability to the business.
Executive Presence & Talent Development: ·
- Model executive presence through clear, confident communication and decisive action.
- Mentor senior leaders and technical teams, fostering a culture of innovation, accountability, and continuous learning.
- Foster a culture of experimentation, agility, and responsible innovation. · Support internal education and AI literacy across the enterprise to empower departments to leverage AI effectively.
- Build and lead high-performing, multidisciplinary teams.
- Develop succession plans and talent pipelines for AI leadership roles.
- Cultivate relationships with external partners, vendors, and thought leaders.
- Represent the organization at industry events, shaping policy and best practices for AI adoption.
Technical Acumen & Innovation: ·
- Direct the design, deployment, and scaling of sophisticated AI/ML solutions, including generative AI, deep learning, and NLP.
- Evaluate and integrate emerging technologies to maintain a competitive edge. · Oversee the development of robust, scalable AI infrastructure, ensuring seamless integration with enterprise data platforms and cloud ecosystems (Azure, Snowflake, Databricks).
- Champion MLOps and responsible AI frameworks.
- Establish and enforce rigorous standards for data governance, security, and ethical AI.
- Lead compliance initiatives for regulated industries (e.g., healthcare, finance, insurance), including GDPR and CPRA.
- Forge strategic partnerships across business units, ensuring AI initiatives deliver measurable value in sales, operations, finance, marketing, and risk management.
- Lead enterprise-wide change management for AI adoption.
Position Requirements (Knowledge, Skills, and Abilities)
- 10+ years of experience in Data platforms, AI, ML, or advanced analytics, with at least 5 years in a senior leadership or enterprise strategy role.
- Proven track record of delivering AI solutions that drive measurable business value.
- Experience working with cloud platforms (e.g., Azure, Snowflake, Databricks), AI/ML tools (e.g., PySpark, Ragas, mlflow, LangChain, etc), and enterprise data platforms.
- Deep understanding of AI/ML techniques (supervised/unsupervised learning, NLP, deep learning, generative AI).
- Strong knowledge of enterprise IT, data governance, architecture, and security standards.
- Exceptional communication and influence skills, with the ability to present complex topics to executive and non-technical audiences.
- Strategic thinker with strong business acumen and a collaborative mindset.
### Preferred Experience:
- Experience implementing Responsible AI frameworks or working within regulated industries (e.g., healthcare, finance, insurance).
- Familiarity with AI compliance (e.g., GDPR, CPRA) and explainable AI practices.
- Background in integrating generative AI solutions (e.g., large language models) in enterprise & sales workflows.
About Integrity
Integrity is one of the nation’s leading independent distributors of life, health and wealth insurance products. With a strong insurtech focus, we embrace a broad and innovative approach to serving agents and clients alike. Integrity is driven by a singular purpose: to help people protect their life, health and wealth so they can prepare for the good days ahead.
Integrity offers you the opportunity to start a career in a family-like environment that is rewarding and cutting edge. Why? Because we put our people first! At Integrity, you can start a new career path at company you’ll love, and we’ll love you back. We’re proud of the work we do and the culture we’ve built, where we celebrate your hard work and support you daily. Joining us means being part of a hyper-growth company with tons of professional opportunities for you to accelerate your career. Integrity offers our people a competitive compensation package, including benefits that make work more fun and give you and your family peace of mind.
Headquartered in Dallas, Texas, Integrity is committed to meeting Americans wherever they are — in person, over the phone or online. Integrity’s employees support hundreds of thousands of independent agents who serve the needs of millions of clients nationwide. For more information, visit Integrity.com.
*Integrity, LLC is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, national origin, disability, veteran status, or any other characteristic protected by federal, state, or local law. In addition, Integrity, LLC will provide reasonable accommodations for qualified individuals with disabilities.*
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 40,900 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Integrity Marketing Group, 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 $154,000 based on 8,743 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Integrity Marketing Group AI Hiring
Integrity Marketing Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 7% (2,924 positions) offer remote work, while 37,845 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 median).
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 40,900 open positions tracked in our dataset. By seniority: 4,029 entry-level, 25,439 mid-level, 7,762 senior, and 3,670 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,924 positions). The remaining 37,845 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 40,900 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (37,169), AI Software Engineer (901), AI Product Manager (900). 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 (4,029) are outnumbered by mid-level (25,439) and senior (7,762) 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 3,670 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,924 positions), with 37,845 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (25,942 postings), Aws (13,683 postings), Rust (11,704 postings), Python (6,016 postings), Azure (4,013 postings), Gcp (3,393 postings), Prompt Engineering (2,293 postings), Kubernetes (1,898 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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