About the Role

We are looking for an AI Engineer to join our Applied AI team to design and build production-grade AI solutions — from LLM applications and agentic workflows to integrations with core business systems. You will work with the team on our current stack, which includes Dify, LangGraph, and LangChain.

Key Responsibilities

• Design and develop LLM-based applications and AI agents / agentic workflows using LangGraph and LangChain

• Build and maintain AI solutions on the Dify platform, including custom tools, workflows, and knowledge pipelines

• Design and improve RAG (Retrieval-Augmented Generation) systems, covering chunking, embedding, retrieval strategy, and evaluation

• Perform prompt engineering and systematically optimize LLM output quality (evaluation, testing, guardrails)

• Integrate AI solutions with internal systems via REST API / Webhook / MCP

• Deploy, monitor, and maintain AI systems in production, including managing cost, latency, and token usage

• Collaborate with Product / Business teams to translate business problems into practical, production-ready AI solutions

• Stay current with emerging AI/LLM technologies, build PoCs, and propose suitable approaches to the team

Qualifications

• Bachelor's degree or higher in Computer Science, Computer Engineering, or a related field (or equivalent experience)

• At least 3 years of software development experience with TypeScript or Python

• Hands-on experience building at least one real-world LLM application (e.g., chatbot, RAG, AI agent) used in production

• Experience with frameworks such as LangChain, LangGraph, or equivalents (e.g., LlamaIndex, Semantic Kernel)

• Solid understanding of key LLM concepts such as context window, embedding, vector search, function/tool calling, and structured output

• Experience working with REST APIs, Docker, and Git

• Able to read technical documentation in English proficiently

Nice to Have

• Experience using or maintaining the Dify platform (or other low-code LLM platforms such as Flowise, n8n)

• Experience with vector databases such as Qdrant or pgvector

• Knowledge of LLM evaluation / observability tools such as LangSmith or Langfuse

• Understanding of MCP (Model Context Protocol) or tool integration for AI agents

• Experience fine-tuning or serving open-source models (e.g., via vLLM, Ollama)

• Basic frontend knowledge (React/Next.js) for building demos or internal tools

Core Technology Stack

• Languages : Python, TypeScript

• AI Frameworks : LangChain, LangGraph (LlamaIndex / Semantic Kernel equivalents accepted)

• AI Platform : Dify / n8n

• RAG & Vector DB : Qdrant, pgvector, embedding & retrieval pipelines

• Evaluation & Observability : Middleware, LangSmith, Langfuse (bonus)

• Integration : REST API, Webhook, MCP

• Deployment : CI/CD, Git, Docker

• Model Serving : vLLM, Ollama (bonus)

• Frontend : React / Next.js (bonus)