# AI Engineer - Agent Development

**Company:** [Makro PRO](http://jobs.workable.com/companies/1cWkogd8purSPWZfcuHmCe.md)
**Location:** Nawamin Road, Thailand
**Workplace:** on site
**Employment type:** Full-time
**Department:** Technology

[Apply for this job](http://jobs.workable.com/view/5413919c-dd3b-4cd2-b0ae-82dd233b23ac)

## Description

The AI Engineer builds production agents end-to-end on an AI-native retail decisioning platform — prompt design, tool definitions, multi-step workflows on the agent runtime (LangGraph, CrewAI, or chosen framework), evaluation harnesses (golden sets, regression gates, multi-step replay), human-in-the-loop gate integration, and per-agent cost optimisation. The role consumes platform-provided LLM and vector services; it does not rebuild that platform. 

**Remote candidates outside of Thailand are welcome to apply.**

### **Key Responsibilities:**

-   Build agents on the platform's agent runtime — prompt design, tool definitions, multi-step workflows, error handling — and ship them with eval harness, human-in-the-loop gate config, observability instrumentation, cost meter, and runbook. 

-   Co-design agent specs with Tech Lead Applications and Suite Product Owners; partner with ML Engineers on classical ML model integration into agents. 

-   Author golden sets per agent — domain-specific test cases capturing must-pass behaviours; build regression gates in CI so no agent ships without eval-pass. 

-   Implement multi-step conversation replay for agents with stateful interactions; use LLM-as-judge patterns where appropriate; instrument human feedback collection. 

-   Configure HITL gates per agent and per agent plan; implement gate-progression evidence collection (Shadow data, accuracy metrics, override frequency). 

-   Own per-agent cost meter — tokens, vector queries, model inference; report monthly; tune model routing and implement caching strategies where appropriate. 

-   Consume the enterprise LLM Gateway via standard SDK; partner with platform AI engineering on embedding model selection and retrieval relevance tuning. 

-   Mentor seed-programme engineers and contribute to the agent-engineering playbook.

## Requirements

-   Bachelor's or Master's degree in Computer Science, AI / ML, or a related discipline. 

-   5+ years software engineering with 2+ years shipping LLM-based or agentic systems to production. 

-   Production agent or multi-step LLM workflow experience — LangGraph, CrewAI, AutoGen, DSPy, or custom. 

-   Strong Python; comfortable with async, observability, testing. 

-   Hands-on with at least one major LLM provider (Azure OpenAI, Anthropic, Bedrock, Vertex). 

-   Eval-driven LLM development — golden sets, LLM-as-judge, regression gates, multi-step replay. 

-   HITL gate / agent governance — has shipped agents with explicit gates, not autonomous-by-default. 

-   Prompt injection / data leakage / PII handling — designs and tests defences. 

**Preferred Qualifications**

-   Open-source contributions to agent frameworks (LangChain / LangGraph / DSPy). 

-   Multi-agent system at scale in production; retail / commerce / fintech agentic workflows (supplier onboarding, contract intelligence, comparable). 

-   Causal inference exposure (DoWhy / EconML); Thai-language NLP (PyThaiNLP, WangchanBERTa, SEA-LION, Typhoon). 

-   Vendor certifications such as Databricks Generative AI Engineer or Azure AI Engineer Associate.