Senior LLMOps Engineer

**Location: Singapore** We are looking for a **Senior LLMOps Engineer** with strong software engineering and infrastructure expertise to join a growing AI engineering team in Singapore. This is a hands\-on **Individual Contributor** role with technical leadership responsibilities. You will help build and operate the infrastructure that supports **LLM training, fine\-tuning, inference, and AI data workflows** , while improving the reliability, scalability, and efficiency of AI workloads across the organization. **Key Responsibilities** * Lead the development and optimization of **model training and fine\-tuning platforms** , including resource scheduling, job management, workflow automation, and troubleshooting. * Build and operate **LLM inference infrastructure** , including deployment of open\-source models and integration with external LLM APIs. * Develop centralized capabilities for **model access, authentication, permissions, quotas, and request management** . * Build and maintain **AI/ML data and model management systems** , covering dataset storage, versioning, quality validation, access control, and traceability. * Develop automation and supporting platforms for the **lifecycle management of models, training jobs, and inference services** . * Optimize **GPU and compute resource utilization** , improving the reliability, performance, scalability, and cost efficiency of AI workloads. * Establish and improve **monitoring, observability, deployment, and operational processes** for AI infrastructure and production services. * Participate in a **24/7 on\-call rotation** , troubleshoot production incidents, and drive continuous improvements in system reliability. * Partner closely with **AI/ML, software engineering, and security teams** to provide infrastructure support and technical guidance for AI adoption. **Requirements** * **5\+ years of experience** in software engineering, platform engineering, SRE, MLOps, or related infrastructure roles. * Hands\-on experience supporting **machine learning model training, fine\-tuning, or inference infrastructure** . * Strong **Python programming** skills, with the ability to independently develop production\-grade platform services and automation tools. * Experience with **Linux, Kubernetes, CI/CD, and observability technologies** . * Familiarity with **GPU infrastructure and resource management** . * Practical understanding of **LLM deployment, model serving, inference workflows, and external LLM API integration** . * Experience with **training data, model artifacts, storage, versioning, access control, and traceability** . * Strong problem\-solving and troubleshooting capabilities, with good technical judgment and cross\-functional communication skills. * Fluent in **English** ; Chinese language proficiency is an advantage. **Preferred Qualifications** * Experience with **distributed model training or large\-scale inference systems** . * Experience with **GPU cluster management and GPU utilization optimization** . * Experience optimizing **LLM inference performance, latency, or infrastructure cost** . * Contributions to **open\-source AI infrastructure, model training, or inference projects** . * Experience with technologies such as **Ray, Kubeflow, MLflow, vLLM, Triton, KServe, Slurm, PyTorch** , or similar AI infrastructure frameworks. **What You Will Work On** **LLMOps · AI Infrastructure · GPU Computing · Model Training · Model Inference · MLOps · SRE · Kubernetes · Python**