AI Infrastructure Engineer — Build Reliable Agent Systems

KAISHI PARTNERS PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

KAISHI PARTNERS PTE. LTD. in Singapore is expanding its engineering team, seeking an Infrastructure Engineer to own and evolve the backend infrastructure for AI agents and stateful workflows. You will be the first infra hire, collaborating with founders on architecture and reliability.

You will build Python-based infrastructure for agent execution, design recovery for interrupted workflows, and connect conversations to outcomes while focusing on latency, costs, and robust deployment practices.

Qualifications

• Strong software engineering skills in Python and backend systems.
• Experience owning production infrastructure and reliability trade-offs.
• Practical understanding of asynchronous processing, state management, APIs, and data persistence.
• Ability to balance rapid delivery with solid architecture.
• Comfort working independently in a small evolving team.

Responsibilities

• Build and evolve Python infrastructure for agent execution, persistent state, model routing, and tool use.
• Design recovery mechanisms for interrupted workflows, including retries and failure handling.
• Develop event and execution-history pipelines linking conversations, decisions, actions, and outcomes.
• Establish deployment, monitoring, and debugging practices for a small team.
• Improve latency, reliability, and model usage costs as the product develops.
• Make pragmatic architectural decisions and own systems through production failures.

Skills

Python

Distributed backend

Production infra

Async processing

State management

APIs

Data persistence

Independent worker

Job description

KAISHI PARTNERS PTE. LTD. in Singapore is expanding its engineering team, seeking an Infrastructure Engineer to own and evolve the backend infrastructure for AI agents and stateful workflows. You will be the first infra hire, collaborating with founders on architecture and reliability.

You will build Python-based infrastructure for agent execution, design recovery for interrupted workflows, and connect conversations to outcomes while focusing on latency, costs, and robust deployment practices.