Freec Asia ★ 1.0

Job Description

RAG System Development

• Design, implement, and optimize end-to-end RAG pipelines, covering data ingestion, indexing, retrieval, reranking, and response generation.
• Integrate, configure, and tune vector databases such as Pinecone, Weaviate, Milvus, or Chroma.
• Improve chunking strategies, embedding quality, and similarity search effectiveness.
• Build evaluation frameworks to measure RAG performance, including precision, recall, context relevance, and answer quality.

Agentic AI & Intelligent Systems

• Develop AI Agents using frameworks such as LangGraph, AutoGen, or LlamaIndex Agents.
• Design and orchestrate multi-agent workflows (e.g., planner, executor, evaluator).
• Implement tool and function calling, serverless integrations, and adaptive reasoning workflows.

Model Context Protocol (MCP)

• Design and implement MCP servers that expose tools, APIs, and datasets to LLMs.
• Integrate MCP across application layers, from frontend and backend services to LLM components.
• Build custom MCP tools, including database connectors, internal API integrations, and data collection utilities.

System Architecture & MLOps

• Build and maintain model inference infrastructure using Docker, Kubernetes, and GPU-based environments.
• Implement logging, monitoring, and observability for RAG and agent-based pipelines.
• Optimize inference performance and cost through techniques such as batching, quantization, vLLM, and TensorRT.

Requirements

• 3 – 5+ years of experience in advanced analytics, machine learning, deep learning, or applying AI/GenAI solutions to real-world business problems.
• Strong proficiency in Python (FastAPI or Flask); experience with Node.js is an advantage.
• Proven experience building production-grade RAG systems.
• Deep understanding of LLMs, embeddings, vector databases, and reranking techniques.
• Hands-on experience with RAG and agent frameworks such as LangChain, LlamaIndex, LangGraph, or equivalent.
• Practical knowledge of Docker, Linux, and foundational DevOps practices.
• Strong debugging, problem-solving, and system-level thinking skills.
• Excellent communication and presentation skills, with the ability to clearly explain AI, LLM, and RAG concepts to both technical and non-technical audiences.
• Ability to deliver AI solution demos and presentations, clearly articulating system architecture, workflows, and business value.
• Fluent spoken and written English.

Preferred

• Experience in client training, presales support, solution presentations, or developing AI educational materials (slides, documentation, demos).
• A teaching and mentoring mindset, with the ability to guide junior engineers and support cross-functional teams such as Product, Business, Sales, and Clients in adopting AI solutions.