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McAfee is hiring a Lead Gen AI Full Stack Engineer to own end-to-end development of production GenAI products on AWS. You will build modern UIs and backend services for our eCommerce platform used by millions of customers, including RAG pipelines and autonomous agents.
Hybrid role in Frisco, TX, onsite 2–3 days weekly; candidates should have 9+ years of full-stack experience and strong cloud, GenAI, and observability skills to ship from prototype to production.
About the Role:
Collaborate with business and engineering stakeholders to translate requirements into technical specifications and execute end-to-end delivery
Engineer the full stack: modern frontends and backend services for our eCommerce platform serving millions of customers, leveraging databases to deliver scalable, high-performance experiences
Work closely with senior leadership to drive architecture decisions and technical direction for GenAI initiatives — developing solutions that improve organizational productivity and customer experience
Design and ship production LLM products — conversational chatbots, AI summarization, operational copilots powered by RAG pipelines (vector search, reranking), and autonomous AI agents with tool orchestration and safety guardrails
About You:
9+ years of full-stack engineering with proven product ownership and large-scale production experience; 2+years shipping production LLM applications with measurable business impact (eCommerce domain experience is a plus)
Expert across modern full-stack: TypeScript, Node.js (NestJS), React 18, Next.js, Python (FastAPI),SQL/NoSQL databases, and big data ecosystem (Spark, Hadoop, Kafka, data lakes)
Strong cloud and infrastructure experience: distributed systems (microservices, event-driven architecture, API design), cloud-native AWS delivery with hands‑on experience in Lambda, ECS/EKS, API Gateway, DynamoDB, RDS, S3, CloudWatch, SQS/SNS, and CI/CD pipelines
Deep GenAI experience: Claude, OpenAI, AWS Bedrock, LangGraph, RAG systems (vector search, reranking), prompt engineering, and evaluation — with hands‑on use of AI coding tools (Claude Code, Cursor, GitHub Copilot) to ship at high velocity
Strong observability expertise — uses metrics, logs, and traces to understand complex distributed systems, identify bottlenecks, surface insights, and resolve production issues across the full stack
#LI-Hybrid
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