Principal AI & Automation Engineer

Comcast Comcast · Media · Philadelphia, PA

This Principal AI & Automation Engineer role focuses on leading QA strategies and working with development for product excellence in cloud-native, microservices-based broadband systems. The role involves architecting intelligent, self-adaptive test automation frameworks using AI/ML-driven testing approaches to transform complex system requirements into scalable, resilient, and insight-driven quality solutions. Key responsibilities include designing self-healing and adaptive test systems, implementing shift-left and shift-right QA strategies, developing data-driven and model-based testing approaches, and integrating QA deeply into DevOps/MLOps pipelines.

What you'd actually do

  1. Architect and build scalable, modular, and AI-augmented test automation frameworks supporting functional, integration, and performance testing.
  2. Design self-healing and adaptive test systems using AI/ML techniques (e.g., anomaly detection, flaky test prediction, intelligent test selection).
  3. Implement shift-left and shift-right QA strategies, embedding quality across the entire SDLC and production monitoring pipelines.
  4. Develop data-driven and model-based testing approaches, leveraging telemetry, logs, and production data to improve test coverage and accuracy.
  5. Lead automation efforts for cloud-native, microservices-based DOCSIS/PON platforms, ensuring high availability and performance at scale.

Skills

Required

  • Strong track record of building enterprise-grade, extensible automation frameworks.
  • Strong proficiency in Linux/Ubuntu environments, CLI tooling, and automation scripting (Python, Go, Shell scripting).
  • Hands-on experience with traffic generation and network test tools (e.g., IXIA, ByteBlower) and simulation platforms.
  • Ability to analyze packet captures, simulate network conditions, and validate end-to-end system performance at scale.
  • Familiarity with observability stacks (logs, metrics, tracing) and applying them to quality engineering.
  • Strong experience with CI/CD pipelines (Jenkins, GitLab CI) and version control (Git), with emphasis on continuous testing and quality gates.
  • Experience integrating AI/ML into QA workflows, such as: Intelligent test case generation, Test optimization and prioritization, Flaky test detection and self-healing frameworks, Anomaly detection in logs, metrics, and network traffic.

Nice to have

  • Familiarity with observability stacks (logs, metrics, tracing) and applying them to quality engineering.
  • Experience with Kubernetes, Docker, and cloud-native test environments, including ephemeral and scalable test infrastructure.
  • Good understanding of networking protocols and systems (TCP/UDP, BGP, ISIS, HTTP/S, multicast, switching/routing).
  • Deep expertise in software architecture and design patterns (OOP, microservices architecture) applied to test systems.
  • Experience with data engineering concepts (test data pipelines, telemetry ingestion) and exposure to MLOps workflows is a plus.

What the JD emphasized

  • intelligent, self-adaptive test automation frameworks
  • AI/ML-driven testing approaches
  • autonomous testing strategies
  • AI-augmented test automation frameworks
  • self-healing and adaptive test systems using AI/ML techniques
  • data-driven and model-based testing approaches
  • predictive quality analytics
  • AI-assisted root cause analysis
  • AI-driven test automation strategies

Other signals

  • AI/ML-driven testing approaches
  • intelligent, self-adaptive test automation frameworks
  • autonomous testing strategies
  • AI-augmented test automation frameworks
  • self-healing and adaptive test systems using AI/ML techniques
  • data-driven and model-based testing approaches
  • predictive quality analytics
  • AI-assisted root cause analysis
  • AI-driven test automation strategies