AI Engineer (GenAI / RAG / LangGraph / Python)
Unison Group Open · verified Oct 22, 2026 At a glance
Mid-level AI Engineering role at Unison Group. Singapore · contract.
Pay not stated AI Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build Python-based GenAI applications, then connect them to business systems and enterprise content
- Improve RAG pipelines so they retrieve useful knowledge and give LLMs stronger context
- Create LangGraph workflows and multi-agent systems, including assistants, chatbots and agentic tools
- Write prompts for different LLM tasks and assess model quality, retrieval results and response accuracy
- Turn business needs into technical AI solutions while following security and responsible AI practices
What you bring
- Strong Python skills and practical experience building applications with generative AI
- Hands-on LangGraph work, plus a solid grasp of RAG and prompt improvement
- Experience connecting enterprise knowledge systems such as Elastic Knowledge or Elasticsearch
- Background with LangChain, LLM integrations, AI agents and multi-step workflows
Who this fits
This mid-level contract role suits an AI engineer who can build LLM applications and connect them to enterprise knowledge and systems. You'll work onsite in Singapore.
From the employer
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.