AI Backend Engineer (AI Workflow Systems)

BJAK is Southeast Asia's largest digital insurance platform, building AI-powered products that simplify insurance and financial services for millions of users. We use AI and automation to transform real-world insurance workflows such as quotations, policy issuance, endorsements, claims, and customer follow-ups.

We are looking for talented AI Backend Engineers to build the systems that power AI-driven decisioning, automation, and orchestration across our platform. This role sits at the core of our AI stack - between models, backend systems, and real users - where latency, correctness, reliability, and cost directly impact production experience.

This is a fully remote position where you will be part of a global engineering team working across multiple countries to build reliable, scalable AI systems.

Focus

• Build and operate backend systems that serve AI-powered insurance workflows in production.

• Design and implement AI orchestration layers that connect models, APIs, workflows, and business logic.

• Build inference pipelines for LLM-based and AI-assisted automation systems.

• Optimize latency, throughput, and cost across AI services (caching, batching, streaming, routing).

• Design stable service boundaries between backend systems, ML components, and product APIs.

• Implement observability: logging, metrics, tracing, alerting, and incident response workflows.

• Debug production issues across distributed AI systems and resolve root causes.

• Collaborate closely with frontend, product, operations, and ML teams to ship end-to-end features.

• Continuously improve system reliability, scalability, and performance.

Ideal Experience

• Strong backend engineering experience in production systems.

• Experience building or operating high-throughput, low-latency services.

• Familiarity with AI systems (LLMs, embeddings, or AI workflows).

• Experience with distributed systems and production debugging.

• Strong understanding of APIs, data flows, and system design principles.

• Experience with observability tools (logging, monitoring, tracing).

• Strong ownership mindset and bias toward shipping.

• Comfortable working in fast-paced, globally distributed teams.

Outcomes

• AI backend systems run reliably at scale with low latency and high availability.

• AI workflows are stable, observable, and production-ready across multiple products.

• System performance improves continuously through real-world feedback and optimization.

• Production incidents are quickly detected, diagnosed, and resolved.

• AI capabilities are seamlessly integrated into global customer and internal workflows.

Tech Stack

• Python

• Node.js

• LLM APIs (OpenAI / Anthropic / open-source models)

• SQL / NoSQL databases

• Kubernetes

• Docker

• Distributed systems tooling

• Observability stacks (logging, metrics, tracing)

How We Work

We believe strong products are built by small, high-ownership teams.

Engineers at BJAK work closely with product, design, AI, and operations teams to solve meaningful real-world insurance problems. We value technical excellence, speed of execution, and practical decision-making.

Engineers are expected to own systems end-to-end—from design and implementation to production reliability and iteration.

Why Join BJAK

• Build AI-Powered Products - Work on intelligent insurance automation systems.

• Global Engineering Organization - Collaborate across multiple countries.

• International Impact - Products used by millions across Southeast Asia and beyond.

• Learning & Development Budget - Support for continuous growth.

• High Ownership Culture - End-to-end ownership of engineering systems.

• Modern Engineering Practices - Focus on scalability and reliability.

• Career Growth - Fast-moving engineering environment.

• Competitive Compensation - Attractive salary package.

• Fully Remote - Work remotely with globally distributed teams.

The Kind of Builder We Want

• Thinks in systems, not just services or endpoints

• Strong ownership of production behavior and system outcomes

• Comfortable working with ambiguity and evolving requirements

• Strong attention to failure modes, latency, and reliability

• Focused on real-world