Senior AI Platform Engineer - Frisco

About the role

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.

Responsibilities

• 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.
• 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.

• Requirements

  • 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.
  • Expertise in 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.

  Qualifications

  Must have a Bachelor's degree in Computer Science, Engineering, or a related field. Additional relevant certifications may be considered.

  Skills

  • Generative AI
  • Platform engineering
  • Cloud-native technologies (AWS, GCP, Kubernetes)
  • CI/CD automation
  • DevSecOps
  • Security controls
  • Event-driven architectures
  • Vector databases
  • Agentic AI concepts

  Benefits

  McAfee offers a comprehensive benefits package including:

  • Bonus Program
  • 401(k) Retirement
  • Medical, Dental, Vision, Basic Life, Short Term Disability and Long-Term Disability Coverage
  • Paid Parental Leave
  • Social Programs
  • Flexible Work Hours
  • Family-Friendly Benefits
  • 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

  Pay

  The anticipated compensation for this position is USD $107,430.00/Yr. - USD $176,490.00/Yr. depending on experience and qualifications.