AI Application & Cloud Operations Engineer (Mid level)

• Operate and monitor enterprise AI applications, including Claude Chat, HE T2A, and Cowork for SEA.

• Manage Linux servers, Docker containers, NGINX, and systemd services in production environments.

• Support and troubleshoot Python-based applications and APIs using Flask, FastAPI, Uvicorn, and Gunicorn.

• Manage and monitor MongoDB, PostgreSQL/pgvector, Redis, and Meilisearch.

• Support integration with AWS Bedrock (Claude) and external AI services such as Tavily API.

• Manage company Cloud Platform VMs running RHEL and Ubuntu.

• Support secure network connectivity using IPsec VPN, AWS PrivateLink, and VPC Endpoints.

• Monitor system health, application performance, and audit activities using CloudWatch and CloudTrail.

• Support file storage, firewall rules, routing, MCP Server, and Filebridge components.

• Develop Python and Bash automation scripts for deployment, monitoring, backup, and recovery.

• Support CI/CD deployment processes and improve operational efficiency.

• Perform incident response, troubleshooting, and root-cause analysis (RCA) and implement preventive measures.

• Maintain technical documentation and operational procedures.

• 5–9 years of experience in application, system, or cloud operations.

• Strong hands-on experience with Linux (RHEL/Ubuntu) administration and production troubleshooting.

• Proficiency in Python and Bash scripting; JavaScript/Node.js experience is a plus.

• Experience operating Flask/FastAPI applications, Docker, NGINX, Uvicorn, or Gunicorn.

• Working knowledge of PostgreSQL and/or MongoDB and related operational tasks.

• Experience with AWS services, preferably AWS Bedrock, CloudWatch, CloudTrail, and VPC networking.

• Understanding of REST APIs, networking, VPN, PrivateLink, and VPC Endpoints.

• Experience with Git and CI/CD tools such as Jenkins.

• Strong troubleshooting, incident management, and problem-solving skills.

Preferred Qualifications

• Experience operating LLM/GenAI applications or RAG-based systems.

• Hands-on experience with AWS Bedrock and Claude.

• Experience with Redis, pgvector, Meilisearch, or other AI application data stores.

• Experience with MCP, AI agents, or AI application frameworks.

• Experience with Kubernetes or container orchestration.

• Experience with Prometheus/Grafana or ELK Stack.

• AWS certification such as Solutions Architect, SysOps Administrator, or DevOps Engineer.

All your information will be kept confidential according to EEO guidelines.