Software Development Engineer, II

kAIgentic is building the intelligence layer for the world's most ambitious enterprises. Headquartered in Singapore with teams in India and Japan, our software platform helps large organizations evolve as fast as technology itself by turning the tacit know-how locked inside their people into safe, governed, AI-powered operations. The hardest part of enterprise transformation is not strategy. It is execution. Institutional knowledge lives in people's heads, systems are fragmented, and risk tolerance is low. kAIgentic captures how work actually happens, designs better workflows, and runs them inside an intelligence layer that is observable, auditable, and engineered for the most regulated environments on earth. The outcome is an enterprise that continuously improves. We are backed by SMBC Group as our founding partner and customer zero, and our platform is already being proven inside one of the most complex, regulated operating environments in the world. That means real problems, real data, and real production impact from Day 1. THE ROLE Own significant components of the Platform Engineering team’s orchestration layer, delivering durable, observable AI workflow systems for enterprise customers. Shape reliability and recovery mechanisms under real-world production load while influencing cross-team design standards. WHAT YOU’LL DO - Own the design and implementation of durable workflow execution on Temporal, ensuring stateful, long-running processes survive failures. - Build LangGraph-based coordination across models and tools, delivering seamless multi-model orchestration. - Design self-correction loops that validate LLM outputs and trigger automated re-prompting on schema mismatches. - Deliver comprehensive observability pipelines using Langfuse and Arize Phoenix, providing end-to-end tracing and metrics. - Improve interrupt-and-resume patterns for human-in-the-loop workflows, enhancing reliability and failure handling for critical components. - Collaborate with cross-functional engineers to embed AI-native development practices and raise code-review rigor. - Build gRPC services and service-mesh integrations that support scalable state management across distributed environments. WHAT YOU’LL BRING - Proven expertise delivering backend and infrastructure systems for enterprise AI workloads. - AI-native velocity as a default mode of working - Deep mastery of Go and Python, writing production-grade code that meets performance and security standards. - Extensive experience with durable execution engines such as Temporal or Cadence, handling long-running workflows. - Strong background in LLM orchestration and self-correcting AI systems, including schema validation and re-prompting strategies. - Solid understanding of distributed systems fundamentals, including consistency, fault tolerance, and scaling. - Hands-on knowledge of observability and tracing stacks, particularly Langfuse and Arize Phoenix, to build…