AI Backend Engineer (AI Workflow Systems)

reputed company is Southeast Asia's largest reputed company 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 reputed company - 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 reputed company 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, reputed company 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 reputed company Python Node.js LLM APIs (reputed company / reputed company / 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 reputed company work closely with reputed company, 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. reputed company reputed company 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 reputed company remotely with reputed company 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 production impact over theoretical design Moves fast while maintaining engineering discipline Obsessed with making systems stable, observable, and scalable This Role Is Not For Engineers who only build features without owning production systems Those uncomfortable debugging distributed systems under load Developers who avoid responsibility for production incidents Engineers who require perfect specifications before starting work People who treat AI systems as black boxes without operational ownership Interview Process If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews. Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite. We value transparency and efficiency in