AI Agent Developer
Designs and builds autonomous AI agent systems that plan, reason, use tools, and execute complex multi-step workflows using LangChain, CrewAI, and Claude Agent SDK. Gartner predicts 40% of enterprise apps will integrate AI agents by end of 2026 (up from <5% in early 2025). Massive talent shortage. Singapore has the world's highest AI job density (3.2%), making it APAC's hottest market for this role.
- Salary Range
- $130K–$250K+
- Hiring Companies
- OpenAI · Microsoft · Salesforce
- Hot Regions
- USASingaporeU.K.ChinaIndiaAustralia
- Core Skills
- Multi-Agent · LangChain · Workflow orchestration
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Job Description Analysis
Core Responsibilities
- Design and build AI agent systems that can autonomously plan, reason, invoke tools, and execute multi-step workflows
- Implement Agent's memory management, tool integration and error recovery mechanism to ensure stable operation in the production environment
- Build a Multi-Agent collaboration framework to allow multiple Agents to work together to complete complex tasks.
- Design the Agent's security boundary and human-AI collaboration interface to ensure that key decisions have human approval
- Build an Agent behavior log and observability system to support debugging, auditing and continuous optimization
Requirements
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- Proficient in mainstream Agent frameworks such as LangChain / CrewAI / AutoGen / Claude Agent SDK
- 3+ years of engineering experience in Python or TypeScript, familiar with asynchronous programming and event-driven architectures
- In-depth understanding of LLM’s Function Calling/Tool Use capabilities and their limitations
- Have production-level API integration experience and be able to interface with enterprise systems such as CRM, ERP, and databases.
- Master Agent evaluation methods to quantify task completion rate, efficiency and reliability
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Nice to Have
- Experience using orchestration engines (Temporal, Airflow)
- Understand the basics of reinforcement learning and planning algorithms
- Experience in implementing enterprise-level AI applications
A Day in the Life
- Morning: Review the Agent execution log, analyze the tasks that failed to run automatically last night and fix them
- Morning: Discuss new Agent workflow requirements with the product manager - automated customer data analysis and report generation
- Afternoon: Implement a new tool interface so that Agent can call the internal CRM system to query customer information
- Afternoon: Optimize the Agent’s reasoning link and reduce redundant tool calls to reduce latency and cost
- Evening: Stress testing edge cases of Multi-Agent collaboration scenarios in a sandbox environment
Career Transition Guide
Suitable Backgrounds
- Back-end engineer: Experience in API development and system integration, with the strongest core advantage in transformationSee this career today: Software Engineer
- DevOps/SRE: Understand distributed systems and observability, directly applicable to Agent operation and maintenance levelSee this career today: DevOps Engineer · Site Reliability Engineer
- Full-stack developer: can handle front-end and back-end at the same time, and build Agent interactive interfaceSee this career today: Full-stack Developer
- Automation test engineer: understand workflow orchestration and exception handling logicSee this career today: QA · IT Support / Automatic Tester
Transition Path
- Step 1: Learn the principles of LLM Function Calling and Tool Use, and understand the core operating mechanism of Agent
- Step 2: Build a single-agent project (such as an automated data collection assistant) with LangChain or CrewAI
- Step 3: Learn Agent memory management, state persistence, and error recovery techniques
- Step 4: Practice Multi-Agent collaboration scenarios—let multiple Agents divide work to complete complex tasks
- Step 5: Build a production-level Agent project and deploy it, accumulate a portfolio and then submit your job
Learning Roadmap
Phase 1: Agent Basics (1-2 months)
Phase 2: Engineering in-depth (February-April)
Phase 3: Production deployment (April-June)
Common Mistakes
Let the Agent run completely autonomously, without human approval and security boundaries
Key decisions must have a Human-in-the-Loop mechanism and set clear authority boundaries.
Solve simple problems with complex Multi-Agent architecture
First use a single Agent + tool call to solve the problem, and only introduce multiple Agents when division of labor is really needed.
Does not monitor Agent’s Token consumption and API call costs
The agent's reasoning loop may cause costs to get out of control, and budget caps and monitoring alarms must be set.
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