Found Description
Responsibilities
- Own the design, development, maintenance, and evolution of the in-house AIOps / ML / LLM platform, including related cloud and on-premise Kubernetes solutions.
- Translate client, security, compliance, and internal requirements into practical platform designs with cross‑functional teams.
- Build and operate production ML / LLM workflows, including retraining, deployment, inference serving, monitoring, rollback, and optimisation.
- Troubleshoot production issues across application, infrastructure, networking, Linux, Kubernetes, and ML serving layers.
- Strong software/platform engineering fundamentals, including system design, API design, distributed systems, scalability, reliability, observability, authentication/authorization, testing, and maintainable code design.
- Practical understanding of the ML / LLM lifecycle, including data pipelines, model training/retra...
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