Senior AI Software Engineer, Data & RAG Platforms

LFX Digital

Hong Kong

On-site

HKD 900,000 - 1,300,000

Full time

48 hours ago
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Job summary

LFX Digital is seeking a Senior AI Software Engineer to join the Strategic Data Unit (SDU) to shape and manage group-level data, AI/ML algorithms, and data connections across the LFX Group.

You will design backend services, build data pipelines, and integrate GraphDB/knowledge graphs, with a strong emphasis on Retrieval-Augmented Generation (RAG) architectures and production readiness.

Qualifications

  • 5+ years of professional software engineering experience with strong Python skills.
  • Experience with API design, testing, and debugging; solid SQL and graph DB knowledge.
  • Proficiency in containerisation and cloud deployment (Docker/Kubernetes); CI/CD.
  • Hands-on with ML frameworks (PyTorch, scikit-learn) and generative AI tools (HuggingFace, LangChain).
  • Experience taking RAG applications from prototype to production.

Responsibilities

  • Design, build, and ship backend services and APIs supporting SDU data and supply chain products.
  • Develop data pipelines and integrations across internal systems and external vendor sources.
  • Own deployed services: monitoring, logging, incident response, root-cause analysis.
  • Refactor, reduce technical debt, and improve reliability and cost efficiency.
  • Maintain CI/CD pipelines and containerised deployments; ensure security and data handling.

Skills

Python
API design
Testing & debugging
Communication
Fast learner
Team collaboration

Education

Bachelor's degree in Computer Science / Engineering

Tools

Docker
Kubernetes
Neo4j
AWS Neptune
GraphDB
HuggingFace
LangChain
PyTorch
scikit-learn
SQL

Job description

LFX Digital is seeking a Senior AI Software Engineer to join the Strategic Data Unit (SDU) to shape and manage group-level data, AI/ML algorithms, and data connections across the LFX Group.

You will design backend services, build data pipelines, and integrate GraphDB/knowledge graphs, with a strong emphasis on Retrieval-Augmented Generation (RAG) architectures and production readiness.

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