Marketing AI Workflow Architect (Houston, TX)

This role is designed as 'Hybrid' with a requirement to work on average 2 days per week from an HPE office.

About the role

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good.

HPE is seeking a technically proficient, business-savvy AI Solutions Architect with a deep understanding of marketing operations, workflows, and data flows. This individual will analyze existing marketing processes, identify opportunities for AI integration, and design end-to-end AI-enabled architectures that enhance productivity and reduce costs. The AI Solution Architect will collaborate closely with AI/ML engineering teams, data engineering, IT, and third-party vendors to develop secure, compliant, and scalable AI solutions aligned with organizational objectives.

Location: This is an onsite position at HPE’s Houston, TX campus on a hybrid schedule of up to 3 days per week in office.

Responsibilities

• Business & Process Analysis
  • 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.
• AI Architecture Alignment & Integration
  • Work closely with the engineering-side AI architecture team to understand the to-be AI architecture and how manual or semi-automated marketing processes are transformed into intelligent, automated solutions — ensuring marketing's business requirements and enterprise-alignment needs are reflected in that design.
  • Maintain a strong working knowledge of the AI solutions being built (LLMs, retrieval-augmented generation, recommendation engines, and other generative AI tools) so enterprise standards, governance, and platform requirements can be mapped onto them — without duplicating the build-side design and development work.
  • Drive the onboarding and integration of AI agents, tools, and applications onto the MarTech stack to enable business processes — spanning both in-house solutions and third-party/bought AI, where this role leads vendor evaluation, build-vs-buy, and enterprise-compliant integration.
  • Review and pressure-test architecture artifacts (context diagrams, data flow diagrams, interface specifications, solution blueprints) produced by the data science and engineering teams — validating enterprise fit, integration, and governance rather than authoring them.
• Workflow & Data Streamlining
  • Collaborate with Data Engineering teams to design data pipelines that facilitate efficient, low-overhead data flow into and out of AI applications, avoiding unnecessary complexity.
  • Ensure data governance, security, and privacy are embedded into all AI solutions.
  • Identify and specify API and integration requirements for AI agents, ensuring interoperability within the broader MarTech ecosystem and with third-party AI vendors.
• Technology Stack & Platform Enablement
  • 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 such as latency, throughput, availability, and resilience for AI services.
  • Influence platform roadmaps and identify gaps in infrastructure or tooling that could hinder AI deployment at scale.
• Vendor & Build vs. Buy Strategies
  • Lead vendor evaluations, perform technical due diligence, and advise on build vs. 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, ensuring they meet compliance and security standards.
• Governance, Risk, and Compliance
  • 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.
• Cross-functional Collaboration
  • Act as the marketing domain representative in enterprise architecture reviews and AI platform working groups.
  • Communicate complex technical solutions effectively to business stakeholders and influence enterprise-wide standards.

Requirements

• A bachelor's degree in computer science, software engineering, or a related field is required. A master's degree or higher in a relevant discipline is preferred.
• Enterprise Architecture & Solution Design: Ability to design scalable, secure, and enterprise-aligned AI and MarTech solutions.
• AI & Data Technologies: Knowledge of modern AI solutions, including large language models (LLMs), generative AI, retrieval-augmented generation, recommendation engines, and AI workflow orchestration.
• Cloud & Infrastructure: Familiarity with cloud platforms (Azure, AWS, GCP), cloud services, networking, environment management, and AI-specific infrastructure components like model endpoints, vector stores, and observability tools.
• Integration & Data Management: Experience with system dependencies, API design, data access patterns, integration standards, and data governance.
• Security & Governance: Understanding of enterprise security practices, privacy, compliance, risk assessment, and vendor due diligence related to AI solutions.
• Communication & Stakeholder Management: Strong ability to communicate complex technical concepts to non-technical stakeholders and influence cross-functional teams.
• Project & Requirements Management: Proven capability to translate business needs into technical requirements, architecture blueprints, and implementation plans.
• Vendor & Platform Evaluation: Experience assessing AI vendors, platforms, and performing technical due diligence.

Certifications (Plus): Certifications in cloud platforms (Azure, AWS, GCP), enterprise architecture frameworks, or AI-related credentials are advantageous.

Benefits

• Health & Wellbeing: Comprehensive suite of benefits supporting physical, financial, and emotional wellbeing for team members and their loved ones.
• Personal & Professional Development: Investment in career growth through programs catered to reaching career goals, whether becoming a knowledge expert or applying skills to another division.
• Unconditional Inclusion: Inclusive work environment celebrating individual uniqueness, with flexibility to manage work and personal needs.

Pay

The expected salary/wage range for this position is USD 119,500 - 275,000 annually in Texas. The listed salary range reflects base salary. Variable incentives may also be offered.

Information about employee benefits offered in the US can be found at myhperewards.com.

Schedule

Hybrid schedule of up to 3 days per week in office at HPE’s Houston, TX campus.