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McAfee

Antivirus and cybersecurity software provider

Applied AI Engineer

AI Application Engineering +1Updated on Nov 10
Full-timeFrisco, TX, USA

About the Job

Requirements

  • You have strong hands-on experience building agents, including with coding agents such as Claude Code, Codex, or similar.
  • You have hands-on experience standing up and managing AI or platform infrastructure, such as API gateways, model-access layers, or comparable production services.
  • You have experience managing or operating an AI gateway or comparable API/model-access gateway.
  • You have proficiency in at least one general-purpose programming language commonly used for building MCP servers and integrations, such as Python and/or TypeScript/Node.js.
  • You have hands-on experience with a major cloud platform, including deploying and operating services in production.
  • You have experience designing or integrating REST APIs, including authentication patterns such as OAuth and API keys, and webhook-based integrations.
  • You have experience with observability and monitoring tooling, including logging, metrics, and alerting.
  • You have working knowledge of security and data-handling fundamentals, including secrets and access management, data-handling policies, and AI-specific risks such as prompt injection and data leakage.
  • You have a strong understanding of the AI model landscape, including the strengths, weaknesses, and appropriate use cases for GPT, Claude, Gemini, and leading open-source models.
  • You are comfortable building and maintaining MCP servers and working with tool-calling patterns.
  • You are able to present and demo to senior leadership, including Directors, VPs, and the CIO.
  • You have strong written communication skills to produce clear, actionable POC findings and Tech Radar updates.
  • You proactively follow AI developments, have tried new frameworks, form opinions on model benchmarks, and can assess whether developments are substantive or hype.

Responsibilities

  • Serve as the point person for AI Center of Excellence infrastructure needs and the platforms that McAfee's AI capabilities run on.
  • Manage the AI Gateway, including logging, model routing, budget thresholds, and cost controls; work with InfoSec on policy and Cloud Infrastructure on the underlying platform to keep it secure, reliable, and cost-attributed.
  • Build and maintain McAfee's MCP factory and skills factory, including reusable MCP servers, skills, and plugins for CoE and business teams.
  • Own the McAfee MCP roster and skills registry; run intake, prioritization, vetting, and publishing with InfoSec, and publish approved skills and MCPs to the AI Tool Hub.
  • Map legacy tools and integrations into the new skills/MCP catalog structure while migrating the company to AI CoE-supported tooling.
  • Evolve AI infrastructure as demand grows by scaling capacity, adding capabilities, and hardening reliability as agent volume increases across the company.
  • Drive model routing across model providers such as AWS Bedrock and Claude Enterprise, partnering with Cloud Infrastructure on reliability and observability as usage scales.
  • Scan AI developments, models, frameworks, agentic patterns, and open-source shifts, and maintain McAfee's AI Tech Radar on a biweekly cadence.
  • Give the Head of AI CoE concise, opinionated assessments of relevant developments, including what they do, whether McAfee should care, and how quickly a proof of concept could be stood up.
  • Recommend and feed the proof-of-concept pipeline, with at least half of candidates originating from the role's own scouting rather than inbound requests.
  • Build and stand up selected proof-of-concept candidates, configure them for McAfee's environment, stress-test them, and report an honest go/no-go recommendation.
  • Prove new capabilities end to end at prototype depth to support recommendations, rather than production operation.
  • Engage InfoSec early throughout proof-of-concept work on security review, data handling, and risk so promising tools clear requirements before advancing.
  • Build custom MCP servers, integrations, and prototypes as needed to test capabilities properly.
  • Deliver technical demonstrations of working prototypes to senior leadership, including VPs and the CIO.
  • Stay current on AI developments and translate that awareness into practical action for the team.

Desired Qualifications

  • Experience managing structured intake, vetting, and publishing pipelines for developer tools or integrations.
  • Experience with infrastructure-as-code such as Terraform and production reliability practices such as monitoring, alerting, and incident response.
  • Experience designing evaluation frameworks or benchmarks to support structured, defensible go/no-go decisions.
  • Experience with enterprise agent platforms.
  • Familiarity with McAfee's technology stack, including AWS, GitHub, Jira, JFrog, Confluence, and Claude.
  • Experience evaluating and recommending AI tools in a structured enterprise context.

About the Company

McAfee

Antivirus and cybersecurity software provider

McAfee creates cybersecurity software to protect computers and other devices from malware and online threats. Its products scan for viruses and malware, monitor systems in real time, and use up-to-date threat intelligence to block risky activity. Many offerings are managed through cloud-based services that let people and organizations deploy protection across multiple devices and users. McAfee distinguishes itself through its long history in antivirus, broad consumer and business product lines, and its move from a standalone company to an Intel-backed security division and back to an independent public company, giving it experience across both home and enterprise markets. The company’s goal is to help people and organizations stay safe online by defending devices, data, and identities from cyber threats.

Company size
5,001-10,000
Company stage
IPO
Headquarters
San Jose, CA
Founded
1987

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