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Lead AI Engineer

hpe · Singapore

New
Senior 🇬🇧 English
Kubernetes MLOps AIOps MLflow Kubeflow Airflow Azure AWS GCP Python FastAPI Flask GitOps Docker Jenkins GitHub Actions LangChain Prometheus Grafana OpenTelemetry

Job description

About the role

Hewlett Packard Enterprise is looking for an experienced Lead AI Engineer to drive the design, development and deployment of AI/ML-powered applications. The role is onsite, with the expectation that you will primarily work from an HPE office in Singapore. You will lead and mentor a team of AI/ML developers, define best practices and deliver scalable, production-grade AI solutions aligned with business goals.

Key responsibilities

  • Design, develop and deploy AI/ML applications, microservices and APIs on Kubernetes-based infrastructure, ensuring scalability, reliability and performance across development, staging and production.
  • Build and maintain end-to-end AI/ML pipelines covering deployment, monitoring, versioning and continuous improvement using modern MLOps/AIOps tools.
  • Lead and mentor a team of AI/ML engineers, conduct code reviews and define best practices.
  • Evaluate and adopt emerging AI tools, frameworks, LLM technologies and open-source solutions to improve platform capabilities and team productivity.
  • Work with Business Analysts, architects and technical teams to align AI engineering with business objectives and ensure secure, compliant solutions.
  • Maintain technical documentation, deployment runbooks and standard operating procedures.

Required profile

  • Bachelor's or Master's degree in Computer Science, Engineering, AI/ML or a related field.
  • 8-10 years of hands-on software engineering experience, focused on AI/ML application development and deployment.
  • Experience leading and mentoring development teams and managing technical priorities.
  • Good understanding of AI security, responsible AI principles and governance frameworks.

Required skills

  • Kubernetes expertise (container orchestration, Helm charts, pod management, scaling and troubleshooting).
  • MLOps/AIOps tools such as MLflow, Kubeflow, Airflow, model registries and monitoring frameworks.
  • Cloud platforms: Azure, AWS or GCP, including their AI services.
  • Strong Python; FastAPI, Flask or similar frameworks are a plus.
  • CI/CD pipelines and tools such as GitOps, Docker, Jenkins or GitHub Actions.
  • GenAI frameworks (e.g. LangChain) and vector databases.
  • Observability tools such as Prometheus, Grafana and OpenTelemetry.

What we offer

  • Benefits supporting physical, financial and emotional wellbeing for team members and their loved ones.
  • Programs for personal and professional development.
  • An inclusive workplace with flexibility to manage work and personal needs.

Questions fréquentes

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Source : ats:workday

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Published 8 hours ago

Expires 1 month from now

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hpe

Singapore