Senior AI Platform Engineer

McAfee

Senior Engineer designing enterprise-grade Generative AI platforms at McAfee. Combining expertise in platform engineering and AI infrastructure for scalable, secure solutions.

Posted 6/14/2026full-timeFrisco • Texas • 🇺🇸 United StatesSenior💰 $107,430 - $176,490 per yearWebsite

Tech Stack

Tools & technologies

ApacheAWSCloudDistributed SystemsGoGoogle Cloud PlatformGRPCKafkaKubernetesMicroservicesPythonTerraform

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

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

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Generative AILLM applicationsAI agentsRAG architecturesKubernetesPythonGoFastAPIgRPCTerraform

Soft Skills

leadershipcommunicationcollaborationproblem-solvinginfluencerisk analysisgovernancedeveloper productivityplatform adoptioncost efficiency

Certifications

AWS Machine Learning SpecialtyGoogle Cloud ML EngineerAWS Solutions ArchitectGCP Solutions ArchitectCKACKADCKS