Engineer 6, Machine Learning, Data & AI

About this role

Make your mark at Comcast -- a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award-winning technology team that turns big ideas into cutting-edge products, platforms, and solutions that our customers love. We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace. Join us. You’ll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on-site collaborating unless the team has been designated as virtual due to the nature of their work. If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.)

Job Summary We are seeking a visionary Senior Technologist, Data & Agentic AI Enablement to shape the future of our enterprise data ecosystem and accelerate the next generation of AI-driven experiences. This enterprise leadership role is responsible for two complementary missions: Preparing enterprise data for an Agentic AI future, ensuring data is trusted, discoverable, semantically rich, governed, and consumable by AI agents. Transforming data engineering through Agentic AI, embedding AI across the data engineering lifecycle to improve productivity, quality, reliability, and speed of delivery. The successful candidate will define the architecture, standards, and engineering patterns that enable AI to become an active participant in the design, development, testing, operation, and optimization of enterprise data platforms. This highly influential role partners closely with Data Engineering, Data Platforms, Enterprise Architecture, Security, Product, and AI teams to accelerate data modernization and AI-enabled business transformation. Job Description

Agentic AI Transformation of Data Engineering: Lead the strategy for integrating Agentic AI across the enterprise data engineering lifecycle, enabling AI-assisted development, operations, and platform management.

Responsibilities include: • Define the enterprise roadmap for incorporating AI agents into data engineering workflows, platform operations, and software delivery. • Partner with platform engineering teams to integrate AI capabilities into CI/CD, Infrastructure-as-Code (IaC), and DevSecOps practices. • Evaluate emerging agent frameworks, copilots, and autonomous engineering platforms for enterprise adoption. • Establish best practices and governance for AI-assisted engineering and operational processes.

Enterprise Data Strategy for AI & Agentic Systems: Develop and drive the enterprise strategy for preparing data assets to support AI, Generative AI, and Agentic AI use cases.

Responsibilities include: • Define principles and reference architectures that enable AI agents to discover, access, understand, and act upon enterprise data safely and effectively. • Partner with business and technology leaders to identify high-value opportunities for Agentic AI solutions. • Establish enterprise standards that align data, AI, and business strategies.

Data Architecture & AI Readiness Lead architectural efforts to ensure enterprise data is optimized for both human and AI consumption.

Responsibilities include: • Establish standards that ensure data is: • Discoverable • Well-described and semantically rich • Governed and trusted • Accessible through standardized interfaces • Consumable by both people and AI agents

• Drive adoption of metadata-driven architectures, semantic models, knowledge graphs, and business ontologies. • Ensure enterprise data products support machine-to-machine interactions in addition to traditional analytics use cases.

Agentic Data Enablement: Define the frameworks that enable AI agents to effectively interact with enterprise data and knowledge assets.

Responsibilities include: • Establish standards for exposing enterprise data through APIs, semantic layers, data products, and retrieval systems. • Partner with platform teams to develop capabilities supporting: • Retrieval-Augmented Generation (RAG) • Agent orchestration platforms • Tool and API discovery • Vector-based retrieval architectures • Context management and memory frameworks

• Develop patterns that allow AI agents to access enterprise knowledge securely and responsibly.

Data Governance & Trust: Ensure governance and trust frameworks evolve to support autonomous and AI-assisted decision making.

Responsibilities include: • Establish controls for data lineage, provenance, quality, explainability, and auditability. • Partner with Security, Privacy, and Risk teams to implement responsible AI controls and secure data access practices. • Define trust frameworks that enable AI agents to operate within approved business guardrails.

Semantic Layer & Knowledge Management: Drive the development of enterprise semantic capabilities that improve data accessibility and AI reasoning.

Responsibilities include: • Lead development of enterprise semantic models and shared business definitions. • Improve metadata quality, business context, and knowledge accessibility across the organization. • Advance enterprise knowledge management practices that enhance AI reasoning, discovery, and decision support.

Platform & Ecosystem Alignment: Collaborate across teams to ensure enterprise platforms support AI-native consumption patterns.

Responsibilities include: • Partner with Data Platform, Engineering, Analytics, and Product teams to align technology roadmaps. • Collaborate with BI and analytics teams to maintain consistent business metrics and semantic definitions across human and AI consumers. • Influence technology investments that enable future AI and Agentic AI capabilities.

Innovation & Thought Leadership Serve as a strategic t

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