• You will architect and implement the core AI pipeline that powers Accel's test creation system. • Work closely with the founder to design and build an AI-powered content generation system from the ground up. • You'll contribute to meaningful parts of the product end-to-end from how the system ingests and understands source material, to how it produces and validates outputs, to how instructors interact with and review what the system generates. • On the engineering side, you'll build and iterate on LLM-driven pipelines, work with retrieval and embedding techniques to ground outputs in real source material and develop backend services and APIs that tie everything together. • Beyond pure coding, you'll be expected to think about output quality and building evaluation steps, catching failure modes, and improving the system based on real instructor feedback. • You'll research new tools and techniques as the AI space evolves and bring relevant ideas directly into the product. • This is a generalist role at an early-stage product where you'll wear multiple hats, work with ambiguity, and have direct input into how things are built.

• Pipeline Development: Build and execute outbound sequences targeting senior decision-makers. • Qualification: Run discovery calls to separate real opportunities from the noise. • Strategy: Work with the founders to refine our ICP and messaging based on real-world feedback. • Stack Optimization: Leverage modern tooling (Clay, Sales Nav, etc.) to automate the manual parts of prospecting so you can focus on the strategy.

• Responsible for researching, designing, developing, and implementing Artificial Intelligence (AI) solutions • Work with identity architects, cybersecurity engineers, data analysts, software developers, and mission stakeholders • Identify opportunities where AI can improve identity analytics, access governance, operational efficiency, cybersecurity, and decision support • Help establish and mature AI capabilities across the ICAM ecosystem by leveraging machine learning, generative AI, predictive analytics, natural language processing, and intelligent automation technologies • Support the full lifecycle of AI initiatives from concept development and experimentation through production implementation and sustainment • Ensure compliance with DoW security requirements and responsible AI principles • Design, develop, test, and implement AI and machine learning solutions supporting enterprise ICAM objectives • Identify opportunities to improve identity governance, access management, onboarding processes, and operational workflows through AI-driven capabilities • Develop predictive analytics, anomaly detection, behavioral analysis, and risk-scoring models using identity and access management data • Collaborate with engineers and architects to integrate AI capabilities into existing ICAM platforms and enterprise services • Evaluate emerging AI technologies and recommend solutions applicable to ICAM, Zero Trust, and cybersecurity use cases • Develop proof-of-concepts, prototypes, and pilot programs to validate AI capabilities before enterprise adoption • Provide technical leadership and subject matter expertise regarding AI technologies and industry best practices.

Role Description This job focuses on leading QA strategies and working with development for product excellence. It entails writing test plans and managing execution. Deep product and system knowledge is required for effective QA. The role improves testing methods and joins design reviews. Contributions to QA innovation and mentoring junior engineers are key. About the Role We’re seeking a Principal AI & Automation Engineer to lead the evolution of our next-generation quality engineering platform. This role is ideal for a highly experienced engineer who can architect intelligent, self-adaptive test automation frameworks for cloud-native, microservices-based broadband systems. You will combine deep automation expertise, modern software architecture, and AI/ML-driven testing approaches to transform complex system requirements into scalable, resilient, and insight-driven quality solutions. As a technical leader, you will drive autonomous testing strategies, enabling faster releases, higher reliability, and continuous quality validation across DOCSIS and PON features on vCMTS ecosystem. What You’ll Do - Architect and build scalable, modular, and AI-augmented test automation frameworks supporting functional, integration, and performance testing. - Design self-healing and adaptive test systems using AI/ML techniques (e.g., anomaly detection, flaky test prediction, intelligent test selection). - Implement shift-left and shift-right QA strategies, embedding quality across the entire SDLC and production monitoring pipelines. - Develop data-driven and model-based testing approaches, leveraging telemetry, logs, and production data to improve test coverage and accuracy. - Lead automation efforts for cloud-native, microservices-based DOCSIS/PON platforms, ensuring high availability and performance at scale. - Collaborate with engineering, product, and QA teams to translate requirements into intelligent, reusable automation assets. - Integrate QA deeply into DevOps/MLOps pipelines, enabling continuous testing, validation, and feedback loops. - Drive Core Virtualization and network validation initiatives, applying advanced simulation, traffic modeling, and AI-assisted diagnostics. - Build tools for predictive quality analytics, identifying risks and failure patterns before they impact production. - Troubleshoot complex distributed systems using advanced observability, AI-assisted root cause analysis, and debugging techniques. - Mentor engineers on modern QA practices, Python architecture, and AI-driven test automation strategies. Qualifications - 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 Additional 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. Benefits - Medical & Dental - 401(k) Savings Plan - Generous paid time off - Life Milestones - from adoption assistance, childcare resources, pet insurance, and more, Comcast supports you at all life stages. - Courtesy Services - We offer all of our full-time employees in serviceable areas free digital TV and internet. - Discount tickets for Universal Resorts, including theme park tickets and onsite hotel rooms. Education - Bachelor's Degree (preferred, but Comcast may consider applicants who hold some combination of coursework and experience, or who have extensive related professional experience). Relevant Work Experience - 10 Years +