{"schemaVersion":"jobsearcher.job.v1","id":"7a546672101e1f0f5bd3a185","url":"https://jobsearcher.com/jobs/7a546672101e1f0f5bd3a185","canonicalUrl":"https://jobsearcher.com/jobs/7a546672101e1f0f5bd3a185","title":"Engineer 6, Machine Learning, Data & AI","description":"Overview\nIn this role you will shape Comcast’s enterprise data ecosystem to enable AI-driven experiences. You will lead two related missions: preparing trusted, discoverable, and governable data for AI agents, and embedding AI across the data engineering lifecycle to boost productivity and delivery speed. You’ll define architecture, standards, and patterns that enable AI to participate in data platform design, development, testing, and operation. You’ll partner with data, security, product, and AI teams to accelerate data modernization and transformation.\n\nCompensation / Benefitsbest-in-class Benefitscompetitive compensationopportunity to influence at scale\nResponsibilitiesLead the strategy to integrate Agentic AI across the data engineering lifecycle to support AI-assisted development, operations, and platform managementDefine the enterprise roadmap for AI agents in data workflows, platform operations, and software deliveryPartner with platform engineering to embed AI in CI/CD, IaC, and DevSecOps practicesEvaluate agent frameworks, copilots, and autonomous platforms for enterprise adoptionEstablish governance and best practices for AI-assisted engineering and operationsDevelop and drive data strategy to prepare assets for AI, Generative AI, and Agentic AI use casesDefine principles and reference architectures for AI agents to discover and act on data safelyCollaborate with business and technology leaders to identify high-value Agentic AI opportunitiesLead data architecture efforts to optimize data for human and AI consumptionEstablish standards for data discoverability, description, governance, and accessibilityPromote metadata-driven architectures, semantic models, knowledge graphs, and ontologiesDefine frameworks for API access, semantic layers, data products, and retrieval systemsSupport capabilities like Retrieval-Augmented Generation, agent orchestration, and vector-based retrievalDevelop patterns for secure and responsible access to enterprise knowledgeEnsure governance and trust evolve to support AI-assisted decision makingEstablish data lineage, provenance, quality, explainability, and auditability controlsWork with Security, Privacy, and Risk to implement responsible AI controlsAdvance semantic capability development and knowledge management for AI reasoningAlign platform roadmaps with Data Platform, Engineering, Analytics, and Product teamsSupport consistent metrics and semantic definitions across human and AI usersAdvise executive leadership on enterprise AI strategy and architectureProvide strategic leadership in AI readiness and data modernization\nKey requirements+ years of experience in Data Architecture, Data Engineering, or Enterprise ArchitectureProven experience designing and scaling enterprise data ecosystemsExperience supporting AI, machine learning, or data modernization initiativesDemonstrated success influencing outcomes in large, matrixed organizationsDeep understanding of modern data architectures (data warehouses, data lakes, lakehouses, data mesh)Expertise in metadata management, semantic modeling, data governance, and data product designStrong understanding of APIs, event-driven architectures, interoperability standardsExperience with AI technologies (LLMs, RAG architectures, vector databases, agent frameworks, AI orchestration platforms)Knowledge of modern software engineering, DevSecOps, CI/CD, and cloud-native architecturesStrong executive communication and stakeholder management skillsAbility to translate emerging technologies into practical enterprise strategiesProven ability to lead through influence across business and technology organizationsDemonstrated thought leadership in data, AI, or enterprise architecture domainsleadershipstakeholder managementstrategic thinkingAgentic AIData ArchitectureData Engineering","company":"Comcast","rawCompany":"comcast","city":"McLean","state":"VA","isRemote":false,"isActive":false,"createdAt":"2026-09-15T03:49:00.277Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Engineer 6, Machine Learning, Data & AI","description":"Overview\nIn this role you will shape Comcast’s enterprise data ecosystem to enable AI-driven experiences. You will lead two related missions: preparing trusted, discoverable, and governable data for AI agents, and embedding AI across the data engineering lifecycle to boost productivity and delivery speed. You’ll define architecture, standards, and patterns that enable AI to participate in data platform design, development, testing, and operation. You’ll partner with data, security, product, and AI teams to accelerate data modernization and transformation.\n\nCompensation / Benefitsbest-in-class Benefitscompetitive compensationopportunity to influence at scale\nResponsibilitiesLead the strategy to integrate Agentic AI across the data engineering lifecycle to support AI-assisted development, operations, and platform managementDefine the enterprise roadmap for AI agents in data workflows, platform operations, and software deliveryPartner with platform engineering to embed AI in CI/CD, IaC, and DevSecOps practicesEvaluate agent frameworks, copilots, and autonomous platforms for enterprise adoptionEstablish governance and best practices for AI-assisted engineering and operationsDevelop and drive data strategy to prepare assets for AI, Generative AI, and Agentic AI use casesDefine principles and reference architectures for AI agents to discover and act on data safelyCollaborate with business and technology leaders to identify high-value Agentic AI opportunitiesLead data architecture efforts to optimize data for human and AI consumptionEstablish standards for data discoverability, description, governance, and accessibilityPromote metadata-driven architectures, semantic models, knowledge graphs, and ontologiesDefine frameworks for API access, semantic layers, data products, and retrieval systemsSupport capabilities like Retrieval-Augmented Generation, agent orchestration, and vector-based retrievalDevelop patterns for secure and responsible access to enterprise knowledgeEnsure governance and trust evolve to support AI-assisted decision makingEstablish data lineage, provenance, quality, explainability, and auditability controlsWork with Security, Privacy, and Risk to implement responsible AI controlsAdvance semantic capability development and knowledge management for AI reasoningAlign platform roadmaps with Data Platform, Engineering, Analytics, and Product teamsSupport consistent metrics and semantic definitions across human and AI usersAdvise executive leadership on enterprise AI strategy and architectureProvide strategic leadership in AI readiness and data modernization\nKey requirements+ years of experience in Data Architecture, Data Engineering, or Enterprise ArchitectureProven experience designing and scaling enterprise data ecosystemsExperience supporting AI, machine learning, or data modernization initiativesDemonstrated success influencing outcomes in large, matrixed organizationsDeep understanding of modern data architectures (data warehouses, data lakes, lakehouses, data mesh)Expertise in metadata management, semantic modeling, data governance, and data product designStrong understanding of APIs, event-driven architectures, interoperability standardsExperience with AI technologies (LLMs, RAG architectures, vector databases, agent frameworks, AI orchestration platforms)Knowledge of modern software engineering, DevSecOps, CI/CD, and cloud-native architecturesStrong executive communication and stakeholder management skillsAbility to translate emerging technologies into practical enterprise strategiesProven ability to lead through influence across business and technology organizationsDemonstrated thought leadership in data, AI, or enterprise architecture domainsleadershipstakeholder managementstrategic thinkingAgentic AIData ArchitectureData Engineering","datePosted":"2026-09-15T03:49:00.277Z","dateModified":"2026-09-15T03:49:00.277Z","hiringOrganization":{"@type":"Organization","name":"Comcast","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"McLean","addressRegion":"VA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"7a546672101e1f0f5bd3a185"},"url":"https://jobsearcher.com/jobs/7a546672101e1f0f5bd3a185"}}