Lead Enterprise AI Data Engineer

McAfee

5d • Hybrid

About The Position

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.

Requirements

• 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 streaming pipelines (Kafka, Apache Beam, or equivalent).
• Practical experience with LLMs and RAG pipelines in a production or near-production context.
• Experience with ML frameworks and techniques: scikit-learn, TensorFlow, contextual bandits, or reinforcement learning.
• Cloud platform experience: AWS and/or Google Cloud.
• Containerization and infrastructure-as-code: Docker and Terraform.
• Strong SQL and data modeling fundamentals.
• Track record of owning complex technical problems end-to-end with minimal oversight.
• Ability to communicate analytical findings and infrastructure health clearly to non-technical stakeholders.
• Deep commitment to code quality, testing, and SDLC best practices.
• Proactive problem-solver with strong analytical and critical thinking skills.
• Passionate about applied ML and emerging AI technologies.
• Strong mentoring and knowledge-sharing capabilities.

Nice To Haves

• Experience with Databricks, Snowflake, or Apache Spark
• Familiarity with Opensearch or vector search technologies
• Research background or publications in ML/data science
• Experience with online learning frameworks (e.g. Vowpal Wabbit)
• Familiarity with Model Context Protocol (MCP) or Anthropic/Copilot models.
• 10+ years of hands-on experience in data engineering, software engineering, or a closely related field is a plus.

Responsibilities

• 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.

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed
• Number of Employees: 501-1,000 employees