Senior Machine Learning Ops Engineer

1 month ago

Singapore, SingaporeSenior

Responsibilities

Own the design, development, maintenance, and evolution of the in-house AIOps, ML, and LLM platform.
Build and operate cloud and on-premise Kubernetes solutions.
Develop production ML and LLM workflows covering retraining, deployment, inference serving, monitoring, rollback, and optimization.
Translate client, security, compliance, and internal requirements into practical platform designs with cross-functional teams.
Troubleshoot production issues across application, infrastructure, networking, Linux, Kubernetes, and ML-serving layers.
Build CI/CD, automation, and MLOps or LLMOps workflows for production ML and LLM systems.

Strong software and platform engineering fundamentals, including system design, API design, distributed systems, scalability, reliability, observability, authentication and authorization, testing, and maintainable code design.
Practical understanding of ML and LLM lifecycles, including data pipelines, model training and retraining, evaluation, experiment tracking, deployment, monitoring, and production feedback loops.
Strong Python development experience and working proficiency in Go and C++ for reading, debugging, maintaining, and extending production codebases.
Strong Linux, networking, and Kubernetes fundamentals, including production troubleshooting, service connectivity, ingress, resource limits, workload debugging, and deployment operations.
Experience designing, deploying, and operating production platforms on AWS, Azure, GCP, or on-premise environments.
Strong communication skills and ability to collaborate with AI, deployment, infrastructure, and security teams.
Preferred experience operating Kubernetes in bare-metal, air-gapped, or restricted on-premise environments.
Preferred experience with MLflow, Kubeflow, vLLM, TensorRT, TGI, or similar ML and LLM platform tools.
Exposure to TypeScript, React, or Java-based services is preferred.