New opportunity

AI workflow Designer

HPE · Singapore

About this role

HPE seeks an AI workflow Designer to lead the design and standardization of enterprise-grade AI-driven workflows. The role develops scalable, reusable multi-agent ecosystems for business process transformation, translates customer requirements and industry trends into AI/ML solutions, and helps drive AI adoption across the organization. It is based in Singapore and is hybrid, with an expectation of working from an HPE office an average of two days per week; the listed schedule is 40 hours per week.

Responsibilities

Establish and govern enterprise-wide AI workflow standards, design principles and best practices for consistency and scalability.
Architect and implement multi-agent workflows that support autonomous decision-making, orchestration and coordination across complex business processes.
Create reusable design playbooks, frameworks and reference architectures. Promote modular, platform-based development and asset reuse across the AI Center of Excellence and broader enterprise.
Explore emerging agentic AI approaches and advance intelligent automation and adaptive workflows. Provide technical leadership on significant projects, participate in cross-functional initiatives, and mentor teams in AI workflow design.

Required background and capabilities:

A bachelor's or master's degree in computer science, engineering, data science, machine learning, artificial intelligence or a closely related quantitative discipline; typically 10–15 years of experience. Strong high-tech supply-chain exposure, process mapping and transformation-project execution are required.
Experience implementing and deploying AI projects and driving AI-first process or solution design. Understand supervised and unsupervised learning, deep learning, reinforcement learning, natural language processing, computer vision and statistical modeling, as well as emerging AI agents.
Be skilled in data preparation and cleaning, including data wrangling, feature extraction, dimensionality reduction, missing-value handling and data-quality improvement. Use data visualization to present model insights, and understand end-to-end model lifecycle management.
Bring experience guiding engineers and in AI research initiatives, publications or open-source contributions. Communicate architectures and design proposals, negotiate options with senior management, and explain complex concepts to technical and non-technical stakeholders. The posting also lists data engineering, MLOps, design thinking, full-stack development, scalability testing and a security-first mindset as additional skills.

HPE describes health and wellbeing benefits and personal and professional development programs; no compensation amount is stated.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI workflow designMulti-agent systemsAgentic AIAI orchestrationIntelligent automationWorkflow architectureProcess mappingBusiness process transformationSupply chainMachine learningSupervised learningUnsupervised learningDeep learningReinforcement learningNatural language processingComputer visionStatistical modelingData wranglingFeature extractionDimensionality reductionData qualityData visualizationModel lifecycle managementData engineeringData scienceMLOpsDesign thinkingFull stack developmentScalability testingTechnical leadership

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Set enterprise workflow standards
HPE calls for enterprise-wide AI workflow standards, design principles and governance to support consistency, scalability and organizational goals.
Architect multi-agent workflows
The role designs and implements ecosystems for autonomous decisions, orchestration and coordination across complex business processes, while exploring emerging agentic approaches.
Transform high-tech supply-chain processes
The posting emphasizes high-tech supply-chain exposure, process mapping and transformation execution. The role translates customer requirements into AI/ML solutions and workflow changes.
Make designs reusable across teams
HPE asks for playbooks, frameworks and reference architectures, plus modular, platform-based asset reuse across the AI Center of Excellence and broader enterprise.
Lead delivery and build capability
The role provides technical leadership on significant work, mentors cross-functional teams and communicates architecture options to senior management. Its stated impact includes faster delivery, lower costs or unmet customer needs.

Likely challenges to think through

Autonomy versus control
Preparation hypothesis, not a reported HPE problem: multi-agent autonomy may be difficult to reconcile with predictable enterprise behavior. Prepare to explain how you would set decision boundaries, test coordination and handle failures without losing the intended automation benefit.
Shared architecture versus process variation
Preparation hypothesis: enterprise standards and reusable assets may need to accommodate distinct supply-chain workflows. Prepare an example approach that separates common components from process-specific decisions and explains when reuse is not worth the constraint.
Promising AI design versus deployable change
Preparation hypothesis: process transformation may be slowed by data-quality gaps and the need to maintain models after deployment. Prepare to connect process mapping, data preparation, lifecycle management and measurable business outcomes in one implementation narrative.
Innovation versus organizational adoption
Preparation hypothesis: emerging agentic techniques may offer capabilities before teams have a consistent way to apply them. Prepare to explain how you would assess a new approach, communicate trade-offs to senior leaders and turn the result into guidance teams can use.

Practice your answers

Technical practice

How would you map a high-tech supply-chain process before redesigning it with AI?

What to coverThe posting calls for supply-chain experience, process mapping and transformation execution. Suggested preparation: explain how you would identify decisions, handoffs, data gaps and measures of process performance before choosing where AI could add value.

How would you establish and govern enterprise-wide AI workflow standards?

What to coverThe role is responsible for standards and design principles that support consistency, scalability and organizational goals. Suggested preparation: discuss what to standardize, how to review exceptions and how to preserve room for different business needs.

How would you design a multi-agent workflow for a complex business process?

What to coverThe posting specifies autonomous decision-making, orchestration and coordination across agents. Suggested preparation: describe agent responsibilities, information passed between them, decision boundaries and how the workflow would handle conflicting or failed outputs.

What belongs in a reusable AI workflow playbook or reference architecture?

What to coverThe role develops playbooks and reference architectures and promotes modular, platform-based reuse across the AI Center of Excellence and enterprise. Suggested preparation: distinguish reusable components and design guidance from process-specific choices, including the trade-off between reuse and flexibility.

How would you choose an AI or ML approach for a workflow decision?

What to coverThe posting names supervised and unsupervised learning, deep learning, reinforcement learning, natural language processing, computer vision and statistical modeling. Suggested preparation: compare approaches based on the decision, available data, evaluation method and complexity rather than assuming an agent is always needed.

How would you prepare imperfect data for an AI-driven workflow?

What to coverThe posting explicitly includes wrangling, feature extraction, dimensionality reduction, missing-value handling and data-quality improvement. Suggested preparation: explain how you would diagnose data issues, choose treatments and check whether those treatments preserve useful information.

How would you manage a deployed workflow when model quality or process outcomes deteriorate?

What to coverThe role calls for AI deployment and familiarity with end-to-end model lifecycle management. Suggested preparation: cover how you would distinguish data, model and workflow problems, then evaluate changes before updating a production design.

How would you evaluate an emerging agentic approach before adopting it?

What to coverThe posting asks this role to explore agentic AI and advance intelligent automation and adaptive workflows. Suggested preparation: compare it with a simpler workflow on decision quality, reliability, operational effort and business value; treat those criteria as proposed evaluation measures, not stated HPE policy.

What would you test before scaling a multi-agent workflow across teams?

What to coverScalability testing and a security-first mindset appear among the additional skills, alongside the responsibility to scale reusable workflows. Suggested preparation: discuss load, failure behavior, access boundaries and the trade-off between centralized controls and team-level flexibility.

How would you translate a customer requirement into an AI/ML design proposal?

What to coverThe role translates customer requirements into solutions and calls for visualizing model insights for technical and non-technical stakeholders. Suggested preparation: show how you would clarify the desired outcome, compare design options and present evidence and limitations in an understandable visual form.

Behavioural practice

Tell me about a time you led a cross-functional process transformation.

What to coverThe posting calls for transformation execution and leadership in cross-functional initiatives. Suggested preparation: use a real situation, the actions you took to coordinate process and technical changes, and the result, including any measurable impact you can substantiate.

Describe a time you negotiated an architecture choice with senior stakeholders.

What to coverThe role includes communicating design proposals and negotiating options at senior management levels. Suggested preparation: explain the situation, the options and trade-offs you presented, your actions in resolving disagreement, and the resulting decision.

Give an example of mentoring engineers on a new technical approach.

What to coverThe posting explicitly includes coaching teams and sharing knowledge in AI workflow design. Suggested preparation: describe the team's starting point, what you did to build capability, and the observable result without claiming credit for others' work.

Tell me about a time you encouraged adoption of a shared design standard.

What to coverEnterprise standards and reusable assets are central responsibilities in this role. Suggested preparation: outline the situation, how you addressed competing team needs, and what changed as a result; distinguish adoption from merely publishing guidance.

Describe a time you assessed a new AI approach before applying it.

What to coverThe posting values keeping current with AI advances and exploring emerging agentic approaches. Suggested preparation: give a real situation, how you assessed the approach against the problem and alternatives, and the outcome, including a decision not to adopt it if that was the result.

Sharpen your knowledge

Topics to prepare

Enterprise AI workflow standards and reuse
Posting: HPE calls for enterprise-wide design principles, governance, reusable playbooks and platform-based asset reuse. Suggested revision exercise: compare two workflow designs against a short set of consistency, scalability and reuse criteria; explain which elements should become shared standards.
Multi-agent orchestration and decision-making
Posting: The role architects multi-agent workflows for autonomous decisions and coordination across complex processes. Suggested revision exercise: diagram how agents divide responsibilities, exchange outputs and resolve conflicting decisions in a hypothetical workflow.
High-tech supply-chain transformation
Posting: Strong high-tech supply-chain exposure, process mapping and transformation-project execution are stated requirements. Suggested revision exercise: take a hypothetical supply-chain process and identify its decision points, handoffs and opportunities for AI-first redesign.
AI and ML method selection
Posting: HPE specifies supervised and unsupervised learning, deep learning, reinforcement learning, natural language processing, computer vision and statistical modeling. Suggested revision exercise: compare which of these approaches would fit three different hypothetical workflow problems and explain the trade-offs.
End-to-end model lifecycle
Posting: Familiarity with the full model lifecycle is linked to robust, scalable and maintainable AI systems. Suggested revision exercise: outline the stages from data preparation through deployment and ongoing maintenance, noting what could fail at each stage.

Skills to revise

Map and redesign a process
Posting: Process mapping, transformation execution and AI-first process design are specified. Suggested practice: draw an as-is and proposed workflow for a hypothetical high-tech supply-chain scenario; mark changed handoffs and the intended business outcome.
Specify a multi-agent workflow
Posting: HPE asks for architecture and implementation of coordinated multi-agent ecosystems. Suggested practice: write a small implementation plan with agent responsibilities, inputs, outputs and orchestration steps; walk through one decision from start to finish.
Draft a reusable design playbook
Posting: The role develops playbooks, frameworks and reference architectures for teams across the AI Center of Excellence. Suggested practice: create a one-page workflow template covering modular components, reuse guidance and design-review criteria.
Prepare and present model data
Posting: Data wrangling, feature extraction, dimensionality reduction, missing-value handling, data quality and visualization are named skills. Suggested practice: clean a small illustrative dataset, document each choice, then sketch a visualization that explains a model insight to a non-technical audience.
Plan deployment and lifecycle checks
Posting: Implementing and deploying AI projects is specified, alongside lifecycle management; scalability testing and a security-first mindset appear under additional skills. Suggested practice: draft a deployment checklist covering data quality, scalability, maintenance and security considerations without assuming a particular tool.
Defend an architecture to stakeholders
Posting: HPE expects communication of design proposals, negotiation with senior management and mentoring across teams. Suggested practice: deliver a brief architecture pitch with two options and their trade-offs, then explain the chosen design separately to a technical team and a non-technical stakeholder.

Your preparation checklist

Build a supply-chain transformation walkthrough
Select a real project you can discuss. Prepare a before-and-after process map showing the customer requirement, your role, AI-first design decisions, deployment, process change and measured business impact. The posting emphasizes high-tech supply-chain experience and transformation execution.
Sketch a multi-agent workflow
For a hypothetical complex business process, draw agent responsibilities, orchestration, decision boundaries, coordination and failure handling. Explain how the design would scale; present it as a practice exercise, not an HPE project.
Draft a reusable workflow playbook
Prepare a one-page example with design principles, modular components, a reference architecture and review criteria. Show how teams could reuse assets while maintaining consistent enterprise standards, as the role calls for.
Trace the data-to-model lifecycle
Create a concise technical walkthrough covering missing values, data quality, feature preparation, model choice, deployment and lifecycle management. Include a sample visualization and explain what it would convey to a non-technical stakeholder.
Assemble a source-backed evidence sheet
Map the posting’s stated background to specific examples you can substantiate: relevant education and experience, deployed AI projects, supply-chain work, research or open-source contributions, and mentoring. Leave gaps visible rather than inventing evidence.
Rehearse a senior-management design discussion
Prepare a short architecture proposal with two options, trade-offs and an intended business outcome such as time-to-market or cost reduction. Practice explaining it to technical and non-technical audiences and describing how you would handle disagreement.
Write questions for HPE
Ask which workflows are priorities, how the AI Center of Excellence governs standards and reuse, and how success is measured for this role. These are questions prompted by the posting, not assumptions about HPE’s current processes.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in Singapore. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…