Lead Enterprise AI Data Engineer

Job Title:

Lead Enterprise AI Data Engineer

Role Overview:

As a Lead Enterprise AI Data Engineer at McAfee, you will play a pivotal role within our data innovation team, taking ownership for designing, building, and operating scalable data pipelines and AI-powered tooling that maximize the value of our data assets. You will combine hands-on technical implementation with strategic thinking, applying machine learning and LLM techniques to transform raw data into actionable products — delivering measurable business value and empowering data-driven decision making throughout McAfee.

This role offers the opportunity to work at the intersection of data engineering and applied AI, building systems that directly impact our ability to protect millions of users worldwide. You will have real ownership over high-impact infrastructure and tooling, working alongside strong data science and engineering teams on problems that matter. It is a collaborative role where you will partner closely with data scientists, product teams, and business stakeholders to solve complex problems and build the infrastructure foundation for advanced analytics and AI at McAfee.

Location Note: This is a Hybrid position located in the US, either San Jose, CA,  Frisco, TX or New York City. You will be required to be on-site 2 to 3 days a week; when you are not working on-site, you will work from your home office. We are only considering candidates within a commutable distance of an office location and are not providing relocation assistance at this time.

About the Role

• Partner with business stakeholders to understand data requirements and translate them into scalable technical solutions that drive operational efficiency and strategic insights.

• Identify opportunities to leverage LLMs, RAG pipelines, and ML systems for new internal capabilities and automation.

• Collaborate with data scientists to enable advanced analytics, predictive modeling, and machine learning initiatives that solve complex business problems.

• Provide regular reporting and findings to stakeholders on pipeline health, model performance, and analytical outputs.

• Design and build scalable batch and real-time streaming data pipelines across cloud environments (AWS, Google Cloud).

• Develop and maintain ETL/ELT pipelines that ingest, transform, and store structured and unstructured data from multiple internal and external sources.

• Lead the design and implementation of stream processing architectures for high-throughput, low-latency use cases using Kafka, Apache Beam, or equivalent.

• Design and build production-grade data services and APIs, owning the full software lifecycle from design through deployment and operation.

• Manage database migrations and ensure optimal data flow and storage across systems.

• Create and maintain well-documented data services and interfaces for efficient data access across the organization.

• Build internal tooling leveraging LLMs and RAG pipelines to process, analyze, and query large data assets at scale.

• Apply ML techniques — including contextual bandits, reinforcement learning, and recommendation systems — to business problems where applicable.

• Develop and deploy production ML systems in collaboration with data scientists, taking models from experimentation to operational reliability.

• Apply software engineering best practices — testing, CI/CD, code review — to data pipelines and ML systems.

• Provision and manage cloud infrastructure using Docker and Terraform to ensure reproducible, scalable deployments.

• Implement data quality frameworks including validation checks, monitoring, and automated recovery strategies to maintain data accuracy, completeness, and freshness.

• Ensure secure and auditable data ingestion processes with appropriate handling of sensitive data and compliance requirements.

• Uphold SDLC best practices across development and delivery stages to ensure reliability, maintainability, and scalability.

• Troubleshoot pipeline and model issues; collaborate with platform teams to optimize performance and recovery strategies.

• Continuously evaluate and implement new technologies — including emerging LLM and Gen AI tooling — to improve data engineering capabilities.

• Mentor junior engineers and contribute to the growth of the data engineering practice.

About You

• 10+ years of hands-on experience in data engineering, software engineering, or a closely related field.

• Strong Python programming skills with production-quality code; Java a plus.

• Hands-on experience building batch and real-time streami