You will design, build and maintain production features, services, integrations and workflow automations for internal enterprise applications.
You will manage, making sound trade-offs across quality, maintainability, security, reliability, cost and delivery speed.
You will contribute to reusable foundations for agentic applications, including APIs, data access, identity, permissions, auditability, human escalation and operational guardrails.
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You will design, build and maintain production features, services, integrations and workflow automations for internal enterprise applications.
You will manage, making sound trade-offs across quality, maintainability, security, reliability, cost and delivery speed.
You will contribute to reusable foundations for agentic applications, including APIs, data access, identity, permissions, auditability, human escalation and operational guardrails.
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Gather Requirements: Collaborate with clients to understand their warehouse management needs and gather detailed requirements for the WMS software implementation.
Software Configuration: Configure the WMS software according to the client's requirements, including defining warehouse layouts, product catalog, order processing rules, inventory management settings, and other relevant parameters.
System Integration: Collaborate with internal technical team to ensure seamless integration of the WMS software with other external systems such as Accounting System, ERP, Transportation Management and/or marketplace platforms such as Shopee/Lazada.
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ParcelDaily is seeking a Quality Assurance (QA) Intern to work closely with developers, product, customer service, and operations to test and improve the ParcelDaily platform. The role involves testing workflows, courier integrations, parcel bookings, payment transactions, tracking updates, and other features used by customers.
Agentic AI ArchitectureDesign and implement production-grade agentic systems using models such as Claude. Agent orchestration, tool calling, function calling, multi-agent architectures, planning and task decomposition, agent memory, context management, state machines and workflow engines, long-running agents, human-in-the-loop systems, autonomous execution, recovery and retry mechanisms, observability, and evaluation. You understand the difference between LLM → Agent → Workflow → Autonomous System, and when each is appropriate.
Agentic Loops & Self-OptimizationA major part of the role is building closed-loop systems: Goal → Plan → Execute → Observe → Evaluate → Learn → Re-plan → Execute. Systems that evaluate their own outputs, detect failed actions, identify root causes, adjust strategies, optimize prompts and tool selection, keep what works, roll back what does not, and improve over time. Reflection, critique, self-evaluation, feedback loops, reward signals, evaluation frameworks, automated experimentation, memory, retrieval, state management.
Claude / LLM EngineeringDeep practical experience with Claude/Anthropic APIs is highly desirable. Tool use, structured outputs, streaming, context management, prompt engineering, system prompts, long-context workflows, model routing, token optimization, latency and cost optimization, context compression, agent memory, LLM evaluation. Experience with other frontier models (OpenAI, Gemini, Llama or equivalent) is a plus.
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