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Applications closed on 10 Oct 2026.
Production Support Engineer - LLM - AI Platforms or Trading - Hedge Fund
TRIBUS
Posted 2 months ago • Closed 10 Oct 2026
Job Description
Responsibilities
Hong Kong
We’re working with a leading global quantitative investment manager where technology, data and engineering are fundamental to how the business operates.
As investment in internal AI capabilities continues to grow, they’re looking for a Production Support Engineer to help operate and improve the platform that provides engineers and users across the firm with access to LLMs and AI tooling.
This is a hands-on role sitting between Production Engineering, SRE and AI infrastructure, with exposure to modern cloud, distributed systems and LLM technologies.
What you’ll be doing
- Own and troubleshoot production issues across the LLM platform and model-provider integrations
- Investigate latency, availability, API, capacity and regional performance issues
- Support AWS/Kubernetes infrastructure and developer tooling
- Build scripting and automation to improve diagnostics and reduce manual work
- Improve monitoring, dashboards and alerting
- Support releases and production changes
- Work with engineering, infrastructure teams and external providers to resolve incidents
What we’re looking for
- Production Support, SRE or Platform Operations experience with genuine ownership of production incidents
- Experience supporting LLM/AI infrastructure OR within trading/hedge funds
- Strong Linux and Windows knowledge
- Python, Bash and/or PowerShell scripting
- AWS, Kubernetes and distributed/API-driven systems
- Monitoring and observability -Grafana, Prometheus or similar
- SQL experience with PostgreSQL, SQL Server or similar
- Strong communication and incident-management skills
Useful additional experience
- LLM gateways, inference APIs, AWS Bedrock, streaming or prompt caching
- AI coding assistants or developer tooling
- Terraform / Ansible
- Multi-region production environments
A strong opportunity for an experienced Production Engineer/SRE to work closer to AI and LLM infrastructure within a highly technical quantitative investment environment.