Senior AI Platform Engineer - Frisco

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.

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