AI Solutions Engineer
Salary: ฿45,000 – ฿65,000 per month
About the role
An AI Solutions Engineer discovers and prioritizes AI use cases across the organization, designs and builds LLM-powered applications and automated workflows, integrates solutions with existing systems and data, drives adoption through pilots and training, measures impact, and ensures compliance with security and privacy policies.
Key responsibilities
Work with department heads and frontline users to understand workflows and pain points, and identify where AI can save time, reduce errors, or grow revenue
Evaluate and prioritize use cases by business value, feasibility, data readiness, and risk, and maintain a clear AI roadmap
Build LLM-powered applications, AI agents, and automated workflows using APIs such as Anthropic Claude, OpenAI, or Google Gemini
Implement tool use / function calling, retrieval-augmented generation (RAG) over internal documents, and structured outputs for reliable system integration
Automate recurring work such as report generation, data summarization, document processing, and customer or lead follow-up
Connect AI solutions to internal systems, including ERP (Microsoft Dynamics AX 2012 / SQL Server), Google Workspace (Sheets, Drive, Apps Script), and messaging channels such as LINE Official Account
Run pilots with real users, gather feedback, and iterate quickly until each solution fits the way people actually work
Create simple user guides, deliver training, and support internal AI champions in each department
Define success metrics before building and report results to management
Monitor solutions in production for accuracy, reliability, and API cost, and continuously fix and improve them
About you
Strong programming skills in Python and/or JavaScript / TypeScript
Solid SQL skills, with the ability to query, join, and validate data from relational databases
Hands-on experience building applications with LLM APIs (e.g., Claude, OpenAI, Gemini), including prompt design, tool use / function calling, and handling structured outputs
Practical understanding of RAG: document ingestion, chunking, embeddings, vector search, and evaluating answer quality
Experience integrating systems via REST APIs and webhooks, including authentication (OAuth, API keys)
Experience with workflow automation tools such as n8n, Make, Power Automate, or Google Apps Script
Familiarity with Git, application deployment (Docker and/or cloud platforms such as AWS, Azure, or GCP), and basic logging and monitoring
Proven track record of at least three AI or automation projects used by real users in day-to-day operations. A portfolio, demo, or detailed case study is required
Ability to translate business problems into technical solutions, and to explain technical concepts clearly to non-technical staff
Fluent Thai for working with internal users, and good English reading and writing for technical documentation