ML Data Engineer

Job Title: ML Data Engineer (RAG & Vector Database Focused)

Working Type: Full Time | On-site

Location: D3, HCMC, Vietnam

Overview

We are seeking an experienced Machine Learning Data Engineer with deep expertise in

Retrieval-Augmented Generation (RAG) systems and vector database–centric data

architectures. This role is data-first, not model-training–first.

The primary responsibility is to design, build, and maintain high-quality, scalable data

pipelines that power RAG systems, including dataset engineering, embedding lifecycle

management, and vector database operations.

The engineer will work closely with AI/ML systems but is not expected to perform model

training or act as an AI trainer. Model training and fine-tuning will be handled separately.

Key Responsibilities

• RAG Data Engineering & Architecture

• Design and maintain RAG-ready data pipelines from raw data ingestion to retrieval-ready

corpora.

• Prepare and structure datasets specifically for retrieval-based systems, ensuring data

quality, relevance, and consistency.

• Implement document preprocessing workflows including:

• cleaning

• normalization

• deduplication

• segmentation and chunking strategies optimized for retrieval.

• Architect scalable RAG data architectures, supporting incremental updates and long-term

maintainability.

• Vector Database & Embedding Lifecycle

• Build and operate vector database pipelines (e.g., Qdrant or similar systems).

• Design vector collections, indexing strategies, and payload/metadata schemas for

ecient filtering and retrieval.

• Manage the full embedding lifecycle, including:

• embedding generation

• versioning

• re-embedding strategies

• re-indexing workflows.

• Optimize retrieval performance through proper data modeling, metadata usage, and

vector index tuning.

• Dataset Engineering for AI & ML Systems

• Prepare high-quality datasets for machine learning and AI systems, with a strong focus

on:

• dataset separation (training, validation, evaluation)

• labeling and annotation workflows

• preventing data leakage.

• Support AI training processes only at the dataset level, without responsibility for model

training, optimization, or architecture design.

• Ensure datasets are structured and documented to be reusable, auditable, and scalable.

Retrieval Quality & Scalability

• Evaluate and improve retrieval quality through systematic analysis of data organization,

chunking logic, and metadata structure.

• Collaborate on defining retrieval evaluation strategies and data-driven improvements.

• Ensure data pipelines and vector databases scale eciently with growing data volumes

and usage demands.

Required Skills and Experience

Core Technical Skills

• Strong experience with Python for data processing, pipeline development, and backend

services.

• Solid background in data engineering, including ETL/ELT concepts and production-grade

data workflows.

• Hands-on experience with vector databases (Qdrant, Pinecone, Weaviate, or similar).

• Deep understanding of embedding-based retrieval systems and vector search concepts.

• Proven experience designing RAG data pipelines and retrieval-focused datasets.

Data & System Design

• Expertise in dataset engineering, including structuring, validation, and lifecycle

management.

• Strong knowledge of metadata design, JSON-based schemas, and structured data

handling.

• Experience working in Linux environments (Ubuntu/Debian), including scripting and

troubleshooting.

• Ability to design scalable and maintainable data architectures for long-term AI systems.

• AI & Machine Learning (Baseline Knowledge)

• Practical understanding of how LLMs and AI systems consume data in RAG workflows.

• Familiarity with AI training concepts only at a high level, sucient to prepare high-quality

datasets.

• No requirement to implement, tune, or manage model training pipelines.

Cultural Values & Working Style

• Data-centric mindset with strong attention to structure, quality, and long-term

maintainability.

• Strong ownership mentality from data ingestion to retrieval readiness.

• Ability to work closely with ML engineers and system architects while maintaining a clear

data engineering focus.

• Preference for clean, scalable solutions over experimental or ad-hoc implementations.

• Clear communication and documentation habits, especially around data design

decisions.