AI Solutions Engineer

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