New opportunity

Lead AI Engineer

HPE · Singapore

About this role

HPE seeks a Lead AI Engineer to design, develop and deploy scalable, production-grade AI/ML applications aligned with business goals. This full-time role is based in Singapore and is designated on-site, with work primarily from an HPE office; the posting specifies 40 hours per week.

Responsibilities

Build AI/ML applications, microservices and APIs on Kubernetes-based infrastructure, maintaining scalability, reliability and performance across development, staging and production.
Develop end-to-end AI/ML pipelines for deployment, monitoring, versioning and continuous improvement using MLOps/AIOps practices.
Lead and mentor AI/ML engineers, review code, set engineering best practices, manage technical priorities and drive delivery.
Evaluate emerging AI tools, frameworks, LLM technologies and open-source solutions. Work with business analysts, architects and technical teams to meet business objectives while ensuring secure, compliant solutions. Maintain technical documentation, deployment runbooks and SOPs.

Required qualifications

Bachelor's or master's degree in Computer Science, Engineering, AI/ML or a related field, plus 8–10 years of hands-on software engineering experience with a strong focus on AI/ML application development and deployment.
Expertise in Kubernetes, including container orchestration, Helm charts, pod management, scaling and troubleshooting; experience with MLOps/AIOps tools and practices such as MLflow, Kubeflow, Airflow, model registries and monitoring frameworks.
Hands-on experience with Azure, AWS or GCP and their AI services; strong Python programming; and experience with CI/CD pipelines and tools such as GitOps, Docker, Jenkins or GitHub Actions.
Experience with GenAI frameworks such as LangChain, vector databases, and AI-workload observability tools including Prometheus, Grafana and OpenTelemetry. Ability to lead development teams and understanding of AI security, responsible AI and governance frameworks.

Preferred

Familiarity with FastAPI, Flask or similar frameworks is a plus. HPE describes health and wellbeing benefits and career-development programs, without specifying compensation.

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

Skills for this role

AI/ML application developmentMLOpsAIOpsKubernetesHelmMLflowKubeflowAirflowModel registriesAzureAWSGoogle Cloud PlatformPythonFastAPIFlaskCI/CDGitOpsDockerJenkinsGitHub ActionsLangChainVector databasesPrometheusGrafanaOpenTelemetryAI securityResponsible AIAI governanceMicroservicesAPI development

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

Production-grade AI services
The role designs and deploys AI/ML applications, microservices and APIs on Kubernetes, with scalability, reliability and performance across development, staging and production. Prepare to explain production architecture and operational decisions, not just model development.
End-to-end MLOps and observability
HPE calls for pipelines covering deployment, monitoring, versioning and continuous improvement, alongside CI/CD and AI-workload observability. Prepare a clear account of how a model change moves through release, monitoring and subsequent improvement.
Technical leadership and delivery
The lead mentors engineers, reviews code, defines best practices, manages technical priorities and drives delivery. Prepare examples of engineering decisions and how review or mentoring improved delivery.
Business alignment and responsible deployment
The role works with business analysts, architects and technical teams to meet business objectives while ensuring secure, compliant solutions. Prepare to explain how requirements become technical choices and where AI security, responsible AI and governance affect those choices.
Platform evolution and operational documentation
HPE expects evaluation of emerging AI tools, LLM technologies and open-source solutions, plus technical documentation, deployment runbooks and SOPs. Prepare to distinguish a justified adoption decision from experimentation and explain how the resulting service would be documented for operation.

Likely challenges to think through

Promoting AI workloads without losing reliability
A likely challenge is balancing release speed with consistent behavior across development, staging and production on Kubernetes. Prepare a hypothetical rollout approach covering versioning, checks, monitoring, troubleshooting and rollback; the posting does not specify HPE’s release process.
Choosing new GenAI tools responsibly
Evaluating LLM frameworks and vector databases may create a trade-off between new capabilities and security, governance, maintainability and operational visibility. Prepare criteria for comparing options and deciding when not to adopt one; this is a preparation hypothesis, not a reported HPE problem.
Balancing hands-on engineering with team leadership
Owning technical work while mentoring engineers and coordinating with analysts and architects can make prioritization difficult. Prepare an example of how to delegate, resolve competing technical priorities and keep delivery tied to a business objective; the posting does not describe a specific team conflict.

Practice your answers

Technical practice

How would you design a production-grade AI/ML application as microservices and APIs on Kubernetes?

What to coverSuggested preparation: Explain service boundaries, Python API design, and how the application would move through development, staging, and production. Discuss scalability, reliability, and performance trade-offs rather than assuming a particular workload.

How would you troubleshoot an AI service that becomes unreliable after a Kubernetes deployment?

What to coverSuggested preparation: Walk through checking pod status, logs, resource limits, scaling behavior, and Helm configuration. Explain how you would isolate a deployment issue from an application issue and restore service safely.

How would you build an end-to-end pipeline for deploying and improving an AI/ML model?

What to coverSuggested preparation: Cover orchestration, model versioning or registries, deployment, monitoring, and feedback into subsequent releases. Explain where tools such as MLflow, Kubeflow, or Airflow could help, without treating every example as mandatory.

How would you make AI/ML releases repeatable across environments?

What to coverSuggested preparation: Discuss container builds, CI/CD checks, configuration management, and controlled promotion between environments. Explain the trade-offs of approaches such as GitOps and pipeline automation, including how you would handle a failed release.

What would you monitor for an AI workload, and how would you investigate a performance regression?

What to coverSuggested preparation: Distinguish service health and latency from model-quality signals, then describe useful metrics, logs, and traces. Relate Prometheus, Grafana, and OpenTelemetry to diagnosis without assuming HPE uses a particular monitoring setup.

How would you evaluate a GenAI application using a framework such as LangChain and a vector database?

What to coverSuggested preparation: Explain retrieval quality, response quality, latency, and operational complexity as evaluation criteria. Discuss when the framework and vector database add value versus a simpler design.

How would you choose among cloud AI services and self-managed components for an AI application?

What to coverSuggested preparation: Compare integration, operational burden, scalability, cost, and security considerations across a proposed design. The posting names Azure, AWS, and GCP but does not specify a required cloud or service, so keep the comparison conditional.

How would you address AI security, responsible AI, and governance in a deployment design?

What to coverSuggested preparation: Discuss access controls, data handling, evaluation, and traceable model or application changes. Explain how you would turn governance concerns into engineering checks without claiming a specific HPE policy.

How would you decide whether to adopt a new LLM tool or open-source framework?

What to coverSuggested preparation: Propose a bounded evaluation against business goals, platform fit, security, maintainability, and team productivity. Explain what evidence would justify adoption and when you would defer it.

What should a deployment runbook and SOP contain for an AI/ML service?

What to coverSuggested preparation: Cover release steps, version identification, monitoring checks, troubleshooting, rollback, and ownership. Explain how you would keep the documents usable as the service and pipeline change.

Behavioural practice

Tell me about a time you mentored engineers while raising code quality.

What to coverSuggested preparation: Use a real situation, the code-review or coaching actions you took, and the resulting change in engineering practice. Clarify your own contribution without taking credit for the whole team's work.

Describe a time you had to resolve competing technical priorities to deliver a project.

What to coverSuggested preparation: Set out the delivery context, how you assessed trade-offs and communicated a decision, and the result. Include what you changed when new information emerged, if relevant.

Tell me about a time you aligned technical work with business and architecture stakeholders.

What to coverSuggested preparation: Give the situation, how you worked through differing objectives with business analysts, architects, or technical teams, and the outcome. Make clear how you checked that the solution addressed the underlying business goal.

Describe a time you raised a security or responsible-AI concern about a proposed solution.

What to coverSuggested preparation: Explain the situation, the concern you identified, and the actions you took to evaluate or mitigate it. State the result and any remaining trade-off rather than implying every concern was eliminated.

Tell me about a production issue where you helped coordinate diagnosis and recovery.

What to coverSuggested preparation: Describe the symptoms and your role, then the actions taken with others to restore service and communicate status. Close with the result and any monitoring, runbook, or process improvement that followed.

Sharpen your knowledge

Topics to prepare

Production AI architecture
The posting calls for scalable, reliable AI/ML applications, microservices and APIs on Kubernetes. As a revision exercise, sketch an architecture spanning development, staging and production, and explain how it handles scaling and failures.
MLOps and AIOps lifecycle
HPE specifies deployment, monitoring, versioning and continuous improvement. Trace a model change from pipeline execution through registry versioning, release and monitoring; explain when you would revert it.
Cloud and delivery choices
Hands-on experience with Azure, AWS or GCP AI services and CI/CD is required. Compare how you would deploy the same AI service on one of those platforms using containers and a CI/CD pipeline, including promotion between environments.
GenAI framework and vector-database trade-offs
The role requires experience with GenAI frameworks and vector databases and involves evaluating LLM technologies. As preparation, diagram a possible LangChain-based workflow and identify what you would measure before recommending it; this workflow is a practice example, not a specified HPE project.
Observability, security and AI governance
The posting names AI-workload observability alongside AI security, responsible AI and governance. Draft a checklist of signals to monitor and questions to ask about data access, output risks and compliance for a production AI service.
Technical leadership and delivery
The lead is expected to mentor engineers, review code, set best practices and manage priorities with business analysts and architects. Rehearse a scenario in which reliability work competes with a new feature; explain the decision, review criteria and stakeholder communication.

Skills to revise

Build a Python AI-service endpoint
The posting requires strong Python skills and AI/ML microservices and APIs; FastAPI or Flask familiarity is a plus. As practice, implement a small inference endpoint with input validation, error handling and tests, then explain how you would make it production-ready.
Deploy and troubleshoot with Kubernetes
Kubernetes expertise, including Helm charts, pod management, scaling and troubleshooting, is explicit. Package a sample service in a Helm chart, scale it, then diagnose a deliberately failing pod and describe the fix.
Version an AI/ML pipeline
MLOps/AIOps pipelines and model registries are named in the posting. As practice, outline or implement a pipeline using a listed example such as MLflow, Kubeflow or Airflow; record a model version and show how deployment and rollback would be tracked.
Automate a cloud release
The role requires cloud-platform experience and hands-on CI/CD. As practice, create a container build and test workflow using a posted example such as GitHub Actions or Jenkins, and explain how it would promote an AI service through staging to production on Azure, AWS or GCP.
Instrument an AI workload
HPE names Prometheus, Grafana and OpenTelemetry for AI-workload monitoring. As practice, add metrics or traces to a sample service and design a dashboard that helps distinguish application errors, latency and workload performance changes.
Review code and write an operational runbook
Code reviews, engineering best practices, deployment runbooks and SOPs are explicit responsibilities. Review a sample AI-service change for reliability and security concerns, then write brief deployment, verification and rollback steps.

Your preparation checklist

Map the stated qualifications to evidence
The posting requires a related degree, 8–10 years of software engineering focused on AI/ML, and hands-on Python and cloud experience. Prepare a one-page evidence map using only experience you can substantiate; note any gaps without overstating them.
Build a production project walkthrough
The role involves delivering scalable AI/ML applications, microservices and APIs. Prepare one project narrative covering the business objective, your decisions, deployment path, measurable results and lessons learned.
Practice a Kubernetes design exercise
The posting names Helm, pod management, scaling and troubleshooting across development, staging and production. Sketch a hypothetical AI API deployment and explain reliability, performance, rollout and failure-recovery tradeoffs.
Diagram an end-to-end model lifecycle
The role calls for deployment, monitoring, versioning and continuous improvement using MLOps/AIOps practices. Prepare a pipeline diagram showing where a model registry, CI/CD, cloud AI services and monitoring would fit; identify tools you have actually used.
Prepare a GenAI evaluation example
The posting mentions LLM technologies, GenAI frameworks and vector databases. Outline how you would compare candidate approaches for a business use case, including evaluation criteria, observability, security and responsible-AI tradeoffs.
Document an operational incident or runbook
HPE asks for AI-workload observability, troubleshooting, deployment runbooks and SOPs. Prepare a short, sanitized example showing signals, diagnosis, mitigation, ownership and what changed afterward.
Rehearse a technical leadership story
The role includes mentoring engineers, code reviews, best practices, delivery priorities and collaboration with analysts and architects. Prepare a specific example explaining your decisions, how you handled competing needs and the outcome.
Draft questions for HPE
Ask which AI workloads and Kubernetes platform the team supports, how it measures production quality, who owns governance decisions, and what delivery priorities this lead would inherit. Confirm how the stated Singapore on-site arrangement works in practice.

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

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