Senior AI Platform Engineer
我们的客户是技术咨询公司，为高科技部门提供运营和工程服务。他们支持云和分布式环境中的平台和基础设施团队，帮助运行可扩展，可靠和生产准备的技术平台。我们的客户正在寻找一名高级 AI 平台工程师来操作，维护和持续改善在本地，AWS 和 GCP 环境中运行的产品人工智能平台。该角色结合了 AI 平台工程，MLOps,Kubernetes,Python,可观测性和生产运作，与 EKS 和库贝流基于机器学习平台上的 AI 类似环境一起工作。你还将在改进工程实践，指导团队成员和帮助塑造平台路线图方面发挥高级作用。关键责任 · 使用 GitOps 和 DevOps 实践部署平台发布和配置变化。·通过日志，计量，监测和可观测工具监控 AI 平台和服务健康。·通过自动化，操作工具，可观测性和自我服务能力提高平台可靠性。·参与事件响应，根源分析和24小时运营轮换。·调查并解决用户，平台，集成和配置相关问题。· 促进平台安全，可靠性和运营工程方面的强标准。· 导师小级工程师学习 Python 基础知识并帮助开发他们的 MLOps 能力。· 在整个工程团队中推动采用 MLOps 最佳实践的发展。· 识别支持生产级人工智能系统所需的工具，技术能力和流程中的缺陷。· 贡献 AI 平台的技术方向和持续发展。资格 · 3年以上经验支持使用 Ray,Jupyter,AWS SageMaker,Kubeflow 或类似平台等技术的生产 AI,ML 或数据平台。· 经验5年以上，包括开发，部署，DevOps 或 MLOps 等 AI/ML 生命周期。·支持 AI/ML 工作流程，应用程序或数据工程管道的 Python 实践经验5+年的经验。·与 Kubernetes 的强大的实用经验，包括管理平台如 AWS EKS 或 Google GKE. ·对微服务架构和服务通信模式的良好理解。·在应用程序崩，资源纠纷，服务延迟方面具有强有力的故障解决技能。
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Our client is a technology consulting company providing operational and engineering services to the high-tech sector. They support platform and infrastructure teams across cloud and distributed environments, helping operate scalable, reliable, and production-ready technology platforms.
The Role
Our client is looking for a Senior AI Platform Engineer to operate, maintain, and continuously improve production AI platforms running on Kubernetes across on-premise, AWS, and GCP environments.
The role combines AI platform engineering, MLOps, Kubernetes, Python, observability, and production operations, working with environments similar to AI on EKS and Kubeflow-based machine learning platforms.
You will also play a senior role in improving engineering practices, mentoring team members, and helping shape the platform roadmap.
Key Responsibilities
- Deploy platform releases and configuration changes using GitOps and DevOps practices.
- Monitor AI platform and service health through logs, metrics, monitoring, and observability tools.
- Improve platform reliability through automation, operational tooling, observability, and self-service capabilities.
- Participate in incident response, root cause analysis, and 24/7 operational rotations.
- Investigate and resolve user, platform, integration, and configuration-related issues.
- Promote strong standards across platform security, reliability, and operational engineering.
- Mentor junior engineers in Python fundamentals and help develop their MLOps capabilities.
- Drive the adoption of MLOps best practices across the engineering team.
- Identify gaps in tooling, technical capabilities, and processes required to support production-grade AI systems.
- Contribute to the technical direction and ongoing development of the AI platform.
Qualifications
- 3+ years of experience supporting production AI, ML, or data platforms using technologies such as Ray, Jupyter, AWS SageMaker, Kubeflow, or similar platforms.
- 5+ years of experience across the AI/ML lifecycle, including development, deployment, DevOps, or MLOps.
- 5+ years of hands-on Python experience supporting AI/ML workflows, applications, or data engineering pipelines.
- Strong practical experience with Kubernetes, including managed platforms such as AWS EKS or Google GKE.
- Good understanding of microservices architectures and service communication patterns.
- Strong troubleshooting skills across application crashes, resource contention, service latency, performance, and scaling issues.
- Experience analysing logs, metrics, monitoring systems, and service-level KPIs within production environments.
Nice to Have
- Exposure to additional AI and data platforms such as Flyte, Hugging Face, Vertex AI, LangChain, Claude Code, or other AI agent platforms.
- Hands-on automation or scripting experience using Bash or Python.
- Relevant Kubernetes or cloud certifications such as CKAD or AWS certifications.
Originally posted on Himalayas