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About This Role
Role Overview: Looking for a role where you can lead, mentor, and still stay hands\-on with cutting\-edge AI and machine learning technologies? Be ready to build a new AI\-powered customer insights capability from the ground up and shape the future of customer experience as we are seeking an innovative and hands\-on Senior Manager, AI/ML Engineering \& Customer Insights to establish and lead a new strategic function focused on transforming customer experience through data\-driven insights and artificial intelligence.
This role sits at the intersection of Engineering, Product, Data, and Customer Experience, with responsibility for building capabilities that aggregate and analyze telemetry data, customer feedback, reviews, support signals, and product usage patterns to identify opportunities that improve customer satisfaction, retention, and acquisition.
You will define the vision, architecture, processes, and operating model for leveraging AI/ML and Generative AI technologies to uncover actionable insights at scale. You will be both a technical leader and people manager, capable of developing proof\-of\-concepts, building production\-grade solutions, and leading a growing team of engineers and data professionals.
The ideal candidate combines deep software engineering expertise with practical AI/ML experience, strong business acumen, and the ability to influence stakeholders across engineering, product, and executive leadership teams.
Location Note: This is a Hybrid position located in Frisco, TX. You will be required to be onsite on a regular basis. We are only considering candidates within a commutable distance to one of the two locations and are not offering relocation assistance at this time. Position Details:
*About the Role:*
- Establish and lead a new AI\-driven Customer Insights function within the engineering organization.
- Define the strategy, roadmap, and technical vision for leveraging AI/ML to improve customer experience and business outcomes.
- Partner with Engineering, Product, Customer Experience, and Leadership teams to identify high\-impact opportunities and prioritize initiatives.
- Build frameworks, processes, and best practices for insight generation, experimentation, and measurement.
- Design and implement AI/ML solutions that aggregate, analyze, and synthesize data from multiple sources including telemetry, application logs, customer reviews, surveys, support interactions, and product feedback.
- Develop machine learning models, algorithms, and analytical frameworks that identify trends, root causes, customer pain points, and product improvement opportunities.
- Leverage Large Language Models (LLMs) and Generative AI technologies to automate insight generation, sentiment analysis, summarization, classification, and recommendation workflows.
- Build proof\-of\-concepts and rapidly validate emerging AI technologies before scaling them into production environments.
- Develop approaches to measure the effectiveness and business impact of AI\-generated insights.
- Architect and build scalable cloud\-native solutions on AWS and/or Azure environments.
- Develop robust data pipelines and processing frameworks capable of handling large\-scale structured and unstructured datasets.
- Lead the development of APIs, services, and platforms that support customer insight generation and consumption.
- Ensure engineering excellence through modern software development practices, security, reliability, and operational scalability.
- Drive adoption of MLOps and AI platform best practices for model deployment, monitoring, governance, and continuous improvement.
- Build, mentor, and lead a high\-performing team of engineers and data professionals.
- Provide technical guidance, coaching, and career development support.
- Participate in hiring, technical assessments, and team\-building activities as the function expands.
- Present findings, recommendations, and business impacts to senior leadership.
- Translate complex technical analyses into actionable insights for non\-technical stakeholders.
- Influence product roadmaps and engineering priorities through data\-driven recommendations.
- Collaborate closely with global teams and stakeholders to align customer experience initiatives across the organization.
*About You:*
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Machine Learning, or a related field.
- 10–15 years of software engineering experience with at least 3\+ years focused on AI/ML, advanced analytics, or data\-driven product development.
- Proven experience leading engineering teams and delivering complex technical initiatives from concept through production.
- Strong hands\-on programming experience with Python and modern software engineering practices.
- Demonstrated experience applying AI/ML techniques to solve real\-world business problems.
- Experience building scalable cloud\-native applications and data platforms in AWS, Azure, or GCP environments.
- Strong understanding of machine learning lifecycle, model deployment, experimentation, and production operations.
- Experience working with large\-scale structured and unstructured datasets.
- Strong stakeholder management, communication, and influencing skills.
- Ability to balance strategic leadership with hands\-on technical contribution.
- Hands\-on experience of Machine Learning, Predictive Analytics, Natural Language Processing (NLP), Large Language Models (LLMs), Prompt Engineering, Retrieval\-Augmented Generation (RAG), and AI platforms such as OpenAI, Anthropic, and Hugging Face, with experience developing AI\-powered solutions including sentiment analysis, summarization, classification, and recommendation systems.
- Strong software engineering experience with Python and/or Golang, including designing and developing RESTful APIs, microservices, distributed systems, and event\-driven architectures.
- Experience building and deploying cloud\-native applications using AWS (EKS, Fargate, S3, Lambda), Azure, or Google Cloud Platform (GCP), with expertise in Docker and Kubernetes for containerization and orchestration.
- Strong knowledge of SQL and NoSQL databases, including Microsoft SQL Server, DynamoDB, MongoDB, and Cassandra, along with experience working with big data technologies such as Apache Spark, Hadoop, and Data Lake architectures.
- Experience with messaging and middleware technologies, including Kafka, Redis, and RabbitMQ, to build scalable, high\-performance distributed applications.
*Success Measures:*
Success in this role will be measured by:
- Creation and successful adoption of a customer insights capability.
- Improvements in customer satisfaction and experience metrics.
- Identification and resolution of customer\-impacting engineering issues.
- Influence on product decisions through actionable insights.
- Delivery of scalable AI\-powered solutions that drive measurable business outcomes.
- Growth, effectiveness, and engagement of the engineering team.
Ideal Candidate Profile
The successful candidate is a builder, innovator, and leader who enjoys creating new capabilities from the ground up. They combine strong engineering fundamentals with modern AI expertise and have the ability to translate large volumes of customer and operational data into meaningful business impact. They are equally comfortable discussing architecture with engineers, product strategy with product leaders, and business outcomes with executive stakeholders.
\#LI\-Hybrid
Company Overview: McAfee is a leader in personal security for consumers. Focused on protecting people, not just devices, McAfee consumer solutions adapt to users’ needs in an always online world, empowering them to live securely through integrated, intuitive solutions that protects their families and communities with the right security at the right moment. Company Benefits and Perks: We work hard to embrace diversity and inclusion and encourage everyone at McAfee to bring their authentic selves to work every day. We offer a variety of social programs, flexible work hours and family\-friendly benefits to all of our employees.:* Bonus Program
- 401k Retirement
- Medical, Dental, Vision, Basic Life, Short Term Disability and Long\-Term Disability Coverage
- Paid Parental Leave
- Support and Community Involvement
- 14 Paid Company Holidays
- Unlimited Paid Time Off for Exempt Employees
- 96 Hours of Sick Time and 120 Hours of Vacation for Non\-Exempt Employees Accrued Each Year
We're serious about our commitment to diversity which is why McAfee prohibits discrimination based on race, color, religion, gender, national origin, age, disability, veteran status, marital status, pregnancy, gender expression or identity, sexual orientation or any other legally protected status. Pay Range: The anticipated compensation for this position is USD $135,910\.00/Yr. \- USD $223,285\.00/Yr. depending on experience and qualifications. Job Applicant Privacy Notice: Please click here to view and download the Job Applicant Privacy Notice, which applies to all McAfee job applicants who are residents of the state of California.
Salary Context
This $135K-$223K range is below the median for AI Engineering Manager roles in our dataset (median: $185K across 13 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,317 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At McAfee, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $244,000 based on 23 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($179K) sits 26% below the category median. Disclosed range: $135K to $223K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
McAfee AI Hiring
McAfee has 1 open AI role right now. They're hiring across AI Engineering Manager. Based in Frisco, TX, US. Compensation range: $223K - $223K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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