AI Engineer, Platforms
SGD 60K-108K (estimate) Mid-level Full Time
Tasks
- Automate container build registry and deployments
- Build MCP services
- Build batch API services
- Build internal AI assisted tools for operations
- Develop and maintain CI/CD pipelines
- Diagnose and troubleshoot performance reliability issues
- Manage AI clusters and storage with infrastructure as code
- Monitor environment health GPU capacity performance cost and security
- Optimize LLM inference workflows for production
- Own inference platform operations and reliability
- Reduce operational toil with automation
- Strengthen observability with logs metrics traces and dashboards
- Use AI tools for engineering tasks
Perks/Benefits
- N/A
Skills/Tech-stack
AI Agent | AI agent orchestration | Agent Orchestration | Autoscaling | Bash | CI/CD | Container Registry | Cost Management | DevOps | Docker | GPU Enabled Infrastructure | GPU scheduling | Incident Response | Inference | Inference Cost | Inference cost management | Infrastructure as Code | Kubernetes | LLM Inference | Language Models | Large Language Models | Latency optimization | Logging | Metrics | Model Context Protocol | Multi-cloud | OAuth | Observability | Python | RAG | REST APIs | Reliability Engineering | Retrieval-Augmented Generation | SGLang | Site Reliability | Site Reliability Engineering | TensorRT-LLM | Terraform | Throughput Optimization | Tracing | Transformers | VLLM | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Bachelor of Science in Computer Science | Bachelor of Science in Information Technology
Roles
AI | AI Engineer | DevOps | DevOps Engineer | Engineer | Reliability Engineer | Site Reliability Engineer
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