Full Stack EngineeringFrontend: React, Next.js, TypeScript, modern component architectures, state management, real-time and streaming AI interfaces, agent activity and execution interfaces, data visualization.Backend: Node.js, TypeScript, Python, REST APIs, GraphQL, WebSockets and streaming, event-driven architectures, background workers, job queues, distributed systems, authentication and authorization.
Distributed SystemsDesign systems that reliably execute thousands or millions of AI and data-processing tasks. Kubernetes, Docker, Cloud Run and serverless, message queues, Redis, Kafka or equivalent, distributed job processing, concurrency management, rate limiting, retries, idempotency, fault tolerance, observability. You know how to build systems that stay reliable when agents fail, APIs time out, models hallucinate, or downstream services go away.
Data & Learning InfrastructureBuild the infrastructure agents need to learn from historical executions. PostgreSQL, BigQuery or equivalent data warehouses, ClickHouse or analytical databases, vector databases, embeddings, retrieval systems, event logs, feature stores, analytics pipelines, data ingestion.
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You will work across the entire stack — from frontend experiences and APIs to distributed systems, data infrastructure, LLM orchestration, agentic workflows, evaluation systems, and autonomous optimization loops.
The ideal candidate has deep hands-on experience with Claude/Anthropic models, agentic architectures, tool-use, multi-step reasoning, autonomous execution loops, and AI-driven self-optimization.
You should be comfortable asking: "How can we make the system improve itself rather than requiring an engineer to manually optimize every workflow?"
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You will work across the entire stack — from frontend experiences and APIs to distributed systems, data infrastructure, LLM orchestration, agentic workflows, evaluation systems, and autonomous optimization loops.
The ideal candidate has deep hands-on experience with Claude/Anthropic models, agentic architectures, tool-use, multi-step reasoning, autonomous execution loops, and AI-driven self-optimization.
You should be comfortable asking: "How can we make the system improve itself rather than requiring an engineer to manually optimize every workflow?"
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Field hero: Lead technical deployments on-site. When a high-profile client needs a custom solution in a factory or a mall, you’re the one who makes it happen.
Scaling: As we expand and add new humanoid brands to our fleet, you will lead the integration of their "brains" into our ecosystem.
A ROS2 Wizard who is comfortable living in the Linux terminal.
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Analytical Problem Solving & Performance Tuning: Leverage advanced analytical techniques to diagnose platform bottlenecks, optimize database queries, improve GraphQL API performance, and enhance frontend speed metrics across SG and MY stores.
Marketplace Integrations: Lead the architectural design and analytical evaluation of integrations with major 3rd-party marketplace platforms (e.g., Shopee), ensuring robust error-handling, logs analysis, and real-time synchronization for inventory, orders, and pricing.
Headless & GraphQL Expertise: Provide deep technical authority on Magento GraphQL APIs and headless architecture, analyzing data payload efficiency and network latency for native Mobile Applications.
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Technical Leadership & Solution Ownership: Lead the technical direction of AI initiatives by driving architecture decisions, establishing best practices, mentoring team members, and guiding the end-to-end delivery of scalable AI solutions.
Collaboration & Delivery: Work with Product Owner, Developers, Quality Engineers and UX Designers to deliver the Virtual Companion. Contribute to sprint planning, code reviews and documentation.
Bachelor’s degree or above, with 5-8 years in software development and relevant experience in leading AI/ML development project
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