About The Role

•      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.