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Hewlett Packard Enterprise

Builds servers, networking and private cloud

Marketing AI Workflow Architect

Business AnalysisConfirmed live in the last 24 hours
Full-timeHouston, TX, USA

About the Job

Requirements

  • A bachelor's degree in computer science, software engineering, or a related field is required.
  • Ability to design scalable, secure, and enterprise-aligned AI and MarTech solutions.
  • Knowledge of modern AI solutions, including large language models, generative AI, retrieval-augmented generation, recommendation engines, and AI workflow orchestration.
  • Familiarity with cloud platforms such as Azure, AWS, and GCP; cloud services, networking, environment management, and AI-specific infrastructure components such as model endpoints, vector stores, and observability tools.
  • Experience with system dependencies, API design, data access patterns, integration standards, and data governance.
  • Understanding of enterprise security practices, privacy, compliance, risk assessment, and vendor due diligence related to AI solutions.
  • Ability to communicate complex technical concepts to non-technical stakeholders and influence cross-functional teams.
  • Proven capability to translate business needs into technical requirements, architecture blueprints, and implementation plans.
  • Experience assessing AI vendors and platforms and performing technical due diligence.

Responsibilities

  • Conduct comprehensive assessments of current marketing workflows, including cross-team handoffs, operational dependencies, and data flows.
  • Map end-to-end marketing and communications processes, identifying bottlenecks, redundancies, and automation opportunities.
  • Understand the data landscape, including sources, quality, and integration points, to inform AI embedding strategies.
  • Work with the engineering-side AI architecture team to understand the target AI architecture and how marketing processes are transformed into automated solutions, ensuring marketing business requirements and enterprise alignment needs are reflected in the design.
  • Maintain working knowledge of AI solutions being built, including large language models, retrieval-augmented generation, recommendation engines, and generative AI tools, so enterprise standards, governance, and platform requirements can be mapped onto them without duplicating build-side design and development.
  • Drive onboarding and integration of AI agents, tools, and applications onto the MarTech stack, including vendor evaluation, build-versus-buy decisions, and enterprise-compliant integration of in-house and third-party AI.
  • Review and pressure-test architecture artifacts produced by data science and engineering teams, validating enterprise fit, integration, and governance rather than authoring them.
  • Collaborate with Data Engineering teams to design data pipelines that facilitate efficient, low-overhead data flow into and out of AI applications while avoiding unnecessary complexity.
  • Ensure data governance, security, and privacy are embedded into AI solutions.
  • Identify and specify API and integration requirements for AI agents to ensure interoperability within the MarTech ecosystem and with third-party AI vendors.
  • Work with IT, platform engineering, and AI teams to specify infrastructure needs, including model hosting, vector stores, storage, orchestration, and monitoring tools.
  • Define and specify performance requirements for AI services, including latency, throughput, availability, and resilience.
  • Influence platform roadmaps and identify infrastructure or tooling gaps that could hinder AI deployment at scale.
  • Lead vendor evaluations, perform technical due diligence, and advise on build-versus-buy decisions, balancing cost, complexity, and strategic fit.
  • Ensure selected AI tools and platforms align with organizational standards, security policies, and enterprise architecture.
  • Coordinate with internal AI/ML teams and external vendors to integrate third-party AI solutions and ensure they meet compliance and security standards.
  • Apply enterprise AI governance frameworks, incorporating privacy, security, safety, and ethical considerations.
  • Assess vendor risks, including data residency, lock-in, and operational support.
  • Collaborate with security, legal, and compliance teams during vendor evaluations and solution design.
  • Act as the marketing domain representative in enterprise architecture reviews and AI platform working groups.
  • Communicate complex technical solutions to business stakeholders and influence enterprise-wide standards.

Desired Qualifications

  • A master's degree or higher in a relevant discipline is preferred.
  • Certifications in cloud platforms such as Azure, AWS, or GCP; enterprise architecture frameworks; or AI-related credentials are advantageous.

About the Company

Hewlett Packard Enterprise

Builds servers, networking and private cloud

Hewlett Packard Enterprise sells servers, storage, networking and hybrid cloud software to large companies, governments and service providers. Its GreenLake platform lets customers run on-premises hardware as a metered cloud service, and its Cray supercomputers run national lab and AI training workloads. The networking business, built on Aruba and Juniper Networks, supplies campus, data-center and routing equipment. Revenue comes from hardware sales, multiyear support contracts, financing and subscriptions. The company traces its roots to Hewlett-Packard, founded in 1939, became a separate public company in 2015 and is headquartered in Houston, Texas.

Company size
10,001+
Company stage
IPO
Headquarters
Houston, TX
Founded
1939

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