Principal Engineer - AI/ML

McAfee•San Jose, CA

1d•Hybrid

About The Position

McAfee is a global leader in online protection, dedicated to making the digital world safer. This role involves leading critical projects, mentoring engineers, and shaping innovative security products through fast, iterative delivery focused on business outcomes. The position emphasizes cloud-native architecture, agentic AI systems, full-stack development, and large-scale internet services. Experience from FAANG or leading Big Tech companies is highly valued. This is a hybrid position located in specific hub offices (Frisco, TX; San Jose, CA; Newport Beach, CA; New York, NY; Toronto or Waterloo, Canada), requiring onsite presence as needed.

Requirements

10+ years of professional software development experience, with a significant portion in a technical leadership role.
Extensive hands-on experience in designing, building, and operating large-scale, distributed internet services.
Proven experience in developing and deploying agentic AI systems and AI/ML models into production environments. This includes familiarity with the end-to-end MLOps lifecycle and modern LLM-based architectures.
Strong proficiency in multiple programming languages such as Python, Java, Go, Rust, C++ or similar.
Deep understanding of full-stack development, including front-end technologies (e.g., JavaScript, TypeScript, Dart, React, Flutter) and back-end technologies (gRPC, protobuf).
Experience with cloud platforms (AWS, Azure, or GCP) and containerization technologies (Docker, Kubernetes) as a primary deployment model.
Advanced Cloud & K8s Infrastructure: Deep expertise in Kubernetes (Helm, ArgoCD), advanced cloud networking (Ingress, Calico), Zero Trust Security (OPA), Service Mesh (Istio/Linkerd), and eBPF (Cilium) for high-performance observability and traffic management.
Solid understanding of database technologies (SQL and NoSQL).
Demonstrated use of AI productivity tools (GitHub Copilot, Claude, ChatGPT, etc.) to accelerate software development.
Bias for action: proven track record of fast, iterative, incremental delivery over analysis paralysis.
Business outcome orientation: focus on delivering value and measurable results, not just technical outputs.
Excellent problem-solving, analytical, and technical troubleshooting skills.
Strong communication, interpersonal, and leadership abilities.
Ability to thrive in a fast-paced, agile environment with a strong hands-on coding presence.

Nice To Haves

Contributions to open-source projects.
Experience working at FAANG (Facebook, Amazon, Apple, Netflix, Google) or other leading Big Tech companies.
Experience with big data technologies (e.g., Data Bricks, Snowflake, Big Query, Spark, Hadoop, Kafka, etc.).
Experience in the cybersecurity domain.

Responsibilities

Lead the design, development, and deployment of scalable, resilient, and high-performance software solutions with a focus on agentic AI/ML capabilities.
Provide hands-on technical leadership throughout the entire software development lifecycle, from concept and design to testing, deployment, and operational support.
Architect and build full-stack applications and services that operate on an internet scale, ensuring high availability and low latency on modern cloud platforms (AWS, Azure, GCP).
Drive the technical vision and strategy for AI/ML-powered features and products, translating business requirements into robust technical designs with rapid iteration and incremental delivery.
Mentor and guide a team of software engineers, fostering a culture of technical excellence, innovation, collaboration, speed, and AI-augmented productivity.
Collaborate closely with product managers, data scientists, researchers, and other engineering teams to deliver impactful solutions aligned with business goals.
Leverage AI-assisted development tools and practices to accelerate delivery (GitHub Copilot, Claude Code, LLM-powered workflows).
Champion best practices in software engineering, including code quality, testing, CI/CD, and DevOps with a pragmatic, outcome-driven approach.
Stay current with emerging technologies and industry trends, particularly in AI/ML, agentic and LLM-based systems, multi-agent architectures, distributed systems, and cloud computing.
Contribute to code reviews, design discussions, and architectural decisions with hands-on involvement in implementation.
Troubleshoot and resolve complex technical issues in production environments.