AI Engineer (GenAI / RAG / LangGraph / Python)

Hong Kong Unison Limited; Kuala Lumpur; On-site; MYR 150,000 - 230,000; Full time

Job summary

Hong Kong Unison Limited seeks an AI Engineer to design and deploy enterprise GenAI solutions in Kuala Lumpur. You will build RAG pipelines, craft prompts, and orchestrate LangGraph-based multi-agent workflows to solve real business problems.

You will integrate Elastic Knowledge/Elasticsearch, LangChain ecosystem, and various LLMs (OpenAI, Azure OpenAI, Claude, Gemini) while ensuring governance and security best practices. Proficiency in Python and REST APIs is essential.

Qualifications

• Experience designing enterprise GenAI applications using Python.
• Hands-on knowledge of RAG and prompt optimization.
• Familiarity with vector databases and enterprise search.
• Experience integrating multiple LLMs (OpenAI, Azure OpenAI, Claude, Gemini).

Responsibilities

• Design and develop enterprise Generative AI applications using Python.
• Build and optimize RAG pipelines for knowledge retrieval.
• Develop AI workflows and multi-agent systems with LangGraph.
• Create prompts and templates for diverse LLM use cases.
• Integrate enterprise knowledge repositories like Elastic Knowledge/Elasticsearch.
• Develop AI assistants, chatbots, and agentic solutions.
• Connect LLMs to enterprise APIs, databases, and docs.
• Improve retrieval quality, context management, and response accuracy.
• Implement vector search and semantic search capabilities.
• Evaluate, fine-tune, and monitor LLM performance.
• Collaborate with stakeholders to map use cases to technical solutions.
• Follow governance, security, and responsible AI practices.

Skills

Python

LangGraph

RAG

Prompt engineering

Generative AI

LangChain

OpenAI integration

Vector databases

REST APIs

Git & CI/CD

Tools

Elasticsearch

Elastic Knowledge

Pinecone

Chroma

FAISS

Weaviate

Milvus

Docker

Job description

We are looking for a highly skilled AI Engineer with strong expertise in Generative AI, Prompt Engineering, Retrieval-Augmented Generation (RAG), LangGraph, Elastic Knowledge, and Python. The ideal candidate should have experience designing, developing, and deploying enterprise-grade AI applications powered by Large Language Models (LLMs) and modern AI frameworks.

The candidate should be capable of building intelligent AI agents, orchestrating complex workflows using LangGraph, integrating enterprise knowledge bases, and developing scalable GenAI solutions for business use cases.

Key Responsibilities

• Design and develop enterprise Generative AI applications using Python.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines for knowledge retrieval.
• Develop AI workflows and multi-agent systems using LangGraph.
• Create effective prompts and prompt templates for various LLM use cases.
• Integrate enterprise knowledge repositories using Elastic Knowledge or Elasticsearch-based knowledge systems.
• Develop intelligent AI assistants, chatbots, and agentic AI solutions.
• Connect LLMs with enterprise APIs, databases, and document repositories.
• Optimize retrieval quality, context management, and response accuracy.
• Implement vector search and semantic search capabilities.
• Evaluate, fine-tune, and monitor LLM performance.
• Collaborate with business stakeholders to understand AI use cases and translate them into technical solutions.
• Follow AI governance, security, and responsible AI best practices.

Required Skills

• Strong Python programming experience.
• Hands-on experience with LangGraph.
• Strong knowledge of Retrieval-Augmented Generation (RAG).
• Experience with Prompt Engineering and prompt optimization.
• Experience developing applications using Generative AI technologies.
• Knowledge of Elastic Knowledge / Elasticsearch for enterprise search and knowledge retrieval.
• Experience with LangChain ecosystem.
• Experience integrating OpenAI, Azure OpenAI, Anthropic Claude, Gemini, or similar LLMs.
• Knowledge of vector databases such as Pinecone, Chroma, FAISS, Weaviate, or Milvus.
• Experience building AI agents and multi-step workflows.
• Strong understanding of REST APIs and microservices.
• Familiarity with Git, Docker, and CI/CD pipelines.