McAfee is seeking a Lead AI/Data Engineer to transform operational analytics for its consumer protection team by building scalable data pipelines and real-time monitoring systems. The goal is to provide critical visibility into performance and reliability health across their large subscriber base, improve product delivery and effectiveness through data analysis, and accelerate code development and shipping cycles via AI integration for both customer-facing products and internal developer productivity tools.
Requirements
- 10+ years data engineering, 3+ years AI/ML experience
- Python proficiency; Flutter/Dart, Swift awareness
- Data engineering: Spark, Kafka, ETL pipelines, data warehousing (Snowflake, BigQuery, Databricks)
- AI/ML: TensorFlow, PyTorch, scikit-learn experience
- LLM integration: OpenAI API, Claude API, LangChain, local OSS models
- Cloud platforms: AWS/Azure/GCP with AI/ML services focus
- Databases: SQL/NoSQL (PostgreSQL, MongoDB, Redis)
Responsibilities
- Design comprehensive data metrics from telemetry and user data for threat analysis and UX optimization
- Build near real-time dashboards for system health, security monitoring, and operational issue detection
- Develop scalable ETL pipelines processing large-volume telemetry across heterogeneous systems
- Create automated data quality validation frameworks
- Integrate AI into existing systems for performance optimization and SDLC automation
- Build predictive analytics and anomaly detection for consumer protection telemetry
- Develop AI-driven solutions to reduce friction and improve product efficacy
Other
- This is a Remote position located in the United States.
- You must live in the United States, we are not offering relocation assistance at this time.
- Help the CPE leadership run weekly consumer analytics deep dive sessions to review field telemetry data around new feature engagement metrics, consumer trends, failures, performance and reliability metrics for all the consumer products.
- Weekly AI training sessions with consumer organization to teach and promote AI-driven engineering capabilities
- Measuring and improving AI-driven software engineering KPIs which promote use of high quality SDL capabilities through AI across all parts of the lifecycle: Product, Arch, UX, Design, Engineering, QA, and Ops)
