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About This Role
*Job Title:*
Senior AI Platform Engineer \- Frisco*Role Overview:*
At McAfee, you’ll create solutions in a fun, challenging environment where innovation is encouraged—and excellence is recognized. You’ll use your awesome skills to help engineering
This role is responsible for designing, building, and scaling enterprise\-grade Generative AI platforms and developer ecosystems. The focus is on enabling secure, scalable, reliable, and production\-ready GenAI capabilities across the organization leveraging LLMs, AI gateways, Kubernetes, and cloud\-native infrastructure.
The role combines deep expertise in platform engineering, AI infrastructure, and generative AI at enterprise scale. It operates with a platform\-as\-a\-product mindset, enabling self\-service AI capabilities through developer portals (e.g., Backstage templates and plugins) to accelerate adoption and standardization.
The engineer will partner closely with Security and Governance teams to embed responsible AI practices, enforce policy\-driven controls, and provide token\-level usage and cost visibility. This role also drives consistency in model access patterns, observability, and lifecycle management of AI services across environments.
This is a Hybrid Position located in Frisco, TX. We are only considering candidates within a commutable distance to the Frisco office. You will be required to be onsite on an as\-needed basis; when not working onsite, you will work from your home office. We are only considering candidates within a commutable distance to the office location and are not offering relocation assistance at this time.*About The Role:*
Design, build, and scale enterprise\-grade Generative AI platforms supporting LLM applications, AI agents, RAG architectures, and multi\-model routing.
- Architect and implement secure, scalable AI infrastructure leveraging cloud\-native technologies (AWS, GCP, Kubernetes, GKE/EKS).
- Enable self\-service AI capabilities for engineering teams through standardized platform services, APIs, and Backstage templates/plugins.
- Build and operate Retrieval\-Augmented Generation (RAG) infrastructure, including embedding pipelines and vector stores (OpenSearch, Aurora pgvector).
- Develop and manage enterprise AI gateway capabilities, including model routing, rate limiting, token tracking, and policy enforcement.
- Integrate GenAI services into CI/CD pipelines and platform workflows to enable seamless deployment and lifecycle management.
- Build observability platforms for GenAI systems, tracking token usage, latency, response quality, failure rates, throughput, and cost visibility.
- Own lifecycle management of Kubernetes\-based AI platforms including upgrades, patching, scaling.
- Define SLIs/SLOs and reliability benchmarks for AI platform services.
- Implement AI security guardrails including PII redaction, prompt injection defenses, and policy\-driven controls.
- Integrate DevSecOps and AI security scanning into deployment pipelines to enforce secure\-by\-design practices.
- Design AI release validation, risk analysis, and governance frameworks for production readiness.
- Build reusable infrastructure modules and platform automation frameworks using Infrastructure as Code (Terraform or equivalent).
- Develop upgrade and patching strategies for AI platforms with minimal downtime and operational risk.
- Ensure platform security posture, compliance, and lifecycle governance across environments.
- Drive multi\-cloud AI platform strategy and lead modernization initiatives across AWS and GCP.
- Partner with Security and Governance teams to enforce responsible AI practices and enterprise standards.
- Drive measurable improvements in developer productivity, platform adoption, and AI cost efficiency through standardized platform capabilities.
*About You:*
- 10\+ years of experience in platform engineering, with hands\-on AI/ML or GenAI platform experience.
- Hands\-on experience with at least one LLM ecosystem (AWS Bedrock, OpenAI, Anthropic).
- Strong Kubernetes experience (EKS/GKE), including GPU scheduling, autoscaling, and multi\-tenant isolation.
- Strong programming expertise in Python and Go; experience building services using FastAPI and gRPC.
- Deep expertise in AWS (IAM, VPC, KMS) and Infrastructure as Code (Terraform).
- Experience building and integrating platforms using Backstage (plugins, templates, self\-service patterns).
- Strong understanding of distributed systems and event streaming (Apache Kafka).
- Expertise in CI/CD automation and platform engineering best practices.
- Experience with multi\-model orchestration frameworks (LangChain, LlamaIndex).
- Exposure to LLMOps / MLOps tooling for model lifecycle management, evaluation, and versioning.
- Experience building or integrating AI agent frameworks and orchestration patterns.
- Familiarity with AI cost optimization strategies (token efficiency, caching, adaptive routing).
- Experience with prompt engineering frameworks, guardrails, and evaluation techniques.
- Exposure to AI model evaluation frameworks (quality scoring, hallucination detection, benchmarking).
- Experience with vector databases beyond OpenSearch (e.g., Pinecone, Weaviate)
- Familiarity with event\-driven architectures for AI workflows (Kafka\-based streaming pipelines).
- Experience exposing platform capabilities as reusable APIs, SDKs, templates, and developer tooling.
- Strong understanding of cloud\-native architectures and microservices design patterns.
- Experience implementing AI security controls, governance frameworks, and risk mitigation.
- Experience with enterprise AI gateway patterns for model access and control.
- Exposure to agentic AI concepts (MCP, A2A, AI agents) and emerging GenAI orchestration patterns.
- Proven ability to lead architecture reviews, drive platform governance, and influence engineering standards.
- Demonstrated experience driving large\-scale engineering transformation initiatives.
- AI/ML certifications such as AWS Machine Learning Specialty, Google Cloud ML Engineer is a plus.
- Cloud architecture certifications (AWS/GCP Solutions Architect) is a plus.
- Kubernetes certifications (CKA, CKAD, CKS) is a plus.
\#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 Plan
- Medical, Dental, Vision, Basic Life, Short Term Disability and Long\-Term Disability Coverage
- Paid Parental Leave
- Support for 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.
The starting pay range for this position is $107,430\.00\-$176,490\.00\. McAfee takes into consideration an individual’s skillset, experience and location in making final salary determinations. For further details, please discuss with the Talent Acquisition Partner.
Salary Context
This $107K-$176K 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 McAfee, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($141K) sits 22% below the category median. Disclosed range: $107K to $176K.
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.
McAfee AI Hiring
McAfee has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Frisco, TX, US, San Jose, CA, US. Compensation range: $176K - $176K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 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.
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