Senior AI Platform Engineer
# Senior AI Platform Engineer **Permhunt** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[AI-Platform-Engineering]` `[MLOps]` `[Cloud-Platform-Engineering]` `[Site-Reliability-Engineering]` `[DevOps]` `[Senior-AI-Platform-Engineer]` `[Senior-AI-Platform-Developer]` `[Senior-ML-Platform-Engineer]` `[Senior-AI-Infrastructure-Engineer]` `[Senior-AI-Software-Engineer]` --- ### About the Role 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
- AI-Platform-Engineering
- MLOps
- Cloud-Platform-Engineering
- Site-Reliability-Engineering
- DevOps
- Senior-AI-Platform-Engineer
- Senior-AI-Platform-Developer
- Senior-ML-Platform-Engineer
- Senior-AI-Infrastructure-Engineer
- Senior-AI-Software-Engineer
Наблюдалась 2026-10-19, впервые 2026-10-19, источник — Himalayas (JSON API).