Marketing AI Workflow Architect (Houston, TX)

About this role

Marketing AI Workflow Architect (Houston, TX)   

This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office.

Who We Are:

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. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.

Job Description:

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 be responsible for analyzing existing marketing processes, identifying opportunities for AI integration, and designing 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 in either HPE’s Houston, TX. campus on a hybrid schedule of up to 3 days per week in office.       

KEY 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 being 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. • Provide feed

REQUIRED EXPERIENCE & EDUCATION: • 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 compo

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