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About This Role
We are looking for an AI-forward marketing operator who lives at the intersection of artificial intelligence, marketing automation, and sales enablement. This is a hands-on role for someone who has been using AI tools — particularly LLMs and AI agents — throughout their education and early career, and who is excited to bring that fluency into a high-impact marketing organization.
As our AI & Marketing Automation Manager, you will be the team's primary expert in working within our AI agent platform — to build prompts, agents, and automated workflows that accelerate marketing campaigns, surface data-driven insights, and equip our sales team with the right content at the right time. You will have direct involvement in how our digital infrastructure, marketing technology stack, and AI capabilities work together to drive revenue.
You will report to the EVP of Marketing and work day-to-day with the VPs of Marketing and Sales leadership, making this a highly cross-functional role where your work directly shapes both top-of-funnel marketing performance and bottom-of-funnel sales outcomes.
Why This Role
This is a rare opportunity to be the person who brings AI fluency into the heart of a marketing organization. You will not just be “using” AI tools — you will be the one who shapes how the entire marketing and sales organization leverages them. If you are someone who gets excited about building the prompt that saves your sales team five hours a week, or the automation that turns a scattered data trail into a clean pipeline report, this is your role.
You will have visibility from the EVP level down, direct collaboration with sales leadership, and the creative and technical latitude to build things that matter.
What You'll Do
- Serve as the subject-matter expert on our AI agent platform — building, refining, and maintaining prompts, skills, workflows, and agents that power marketing and sales processes.
- Design and iterate on prompt strategies for content generation, lead scoring, competitive analysis, and sales collateral production.
- Build reusable AI workflows and agents that automate repetitive marketing and sales tasks.
- Continuously evaluate new AI capabilities within our AI agent platform and the broader LLM landscape, and prototype ways to apply them to marketing and sales use cases.
- Maintain documentation and best-practice guides so the broader marketing team can leverage our AI agent platform effectively with your support.
Data Analysis & Reporting
- Analyze marketing and sales data from Salesforce, Marketing Cloud, web analytics, and other platforms to identify trends, opportunities, and performance gaps.
- Build dashboards, reports, and AI-assisted analyses that give marketing and sales leadership real-time visibility into campaign performance, pipeline health, and content effectiveness.
- Use our AI agent platform and other AI tools to accelerate data analysis — turning raw CRM and marketing data into actionable narrative insights for stakeholders.
Required Qualifications
- 2–4 years of experience in marketing operations, marketing automation, sales enablement, revenue operations, or a related analytical/marketing role — or equivalent demonstrated experience through internships, projects, and AI-driven work.
- Demonstrated fluency with AI tools and LLMs — you have been prompting, building workflows, and applying AI to business, marketing, or sales problems throughout your education and early career. You think naturally about how AI can automate and enhance workflows.
- Strong analytical skills — you are comfortable working with marketing and sales data, building reports, and turning numbers into stories that leadership can act on.
- A collaborative, proactive mindset — you enjoy working across teams (marketing, sales, web dev) and can translate between technical and non-technical stakeholders.
- Would be nice to have experience building AI agents or workflows on platforms like Kaji, LangChain, n8n, Zapier, or similar automation/orchestration tools.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Integrity Marketing Group AI Hiring
Integrity Marketing Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Scottsdale, AZ, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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