AI & Data Engineer

In this role, you will contribute to the development of next-generation intelligent systems by architecting agentic workflows that can reason, remember, and act.

📍 Bali, Indonesia (On-site)

REQUIREMENTS

Programming: Mastery of Python (FastAPI, Pydantic, Pandas, ScykitLeran, Pyspark).
AI Frameworks: Hands-on experience with LangChain, LangGraph, or similar libraries focused on agentic behavior. Understanding of MCP.
Experience with Vector Databases (e.g., Pinecone, Milvus, Weaviate, or pgvector).
Strong proficiency in Relational Databases (PostgreSQL / Snowflake / BigQuery).
Data Warehousing: Clear understanding of Star/Snowflake schemas, Fact/Dimension modeling, and modern data stack principles.
NLP: Proven experience with Hugging Face ecosystem, BERT-family models, and intent classification techniques.
ETL: Able to create & maintain ETL jobs with Python & Airflow.

KEY RESPONSIBILITIES

1. AI & LLM Orchestration

Agentic Systems: Design and implement autonomous agents capable of multi-step reasoning, planning, and tool use.
State & Memory Management: Develop robust mechanisms for maintaining conversation context and long-term memory in LLM workflows.
MCP Integration: Implement the Model Context Protocol (MCP) to standardize how our AI models interact with external data sources, tools, and local environments.
Retrieval Augmented Generation (RAG): Build and optimize RAG pipelines using vector databases to provide LLMs with relevant, real-time context.

2. Data Engineering, Analytics & Machine Learning

Analytical SQL: Write complex, high-performance SQL for data analysis, transformation, and feature extraction.
Data Modeling: Design and maintain Dimension and Fact tables within a structured Data Warehouse environment.
Feature Engineering: Identify and extract key features from diverse datasets to improve model performance and business insights.
Pipeline Development: Build scalable ETL/ELT processes that ensure data quality and availability for both AI models and BI tools.
Ability to perform Exploratory Data Analysis (EDA) to inform model architecture.
Supervised & Unsupervised Learning: Implement and evaluate models across the ML spectrum, from predictive regressions to discovery-based clustering.
Classification & Regression: Understanding of basic ML techniques to support data driven analytics.
Recommendation Engines: Design and deploy recommendation systems (collaborative filtering, content-based, or hybrid).
Clustering: Use unsupervised techniques to identify patterns in dataset features and segment user behavior.

3. Natural Language Processing (NLP)

Implement and fine-tune NLP models for tasks such as Named Entity Recognition (NER) and Intent Translation.
Work with transformer-based architectures like BERT to enhance text understanding and classification within our product ecosystem.

PREFERRED QUALIFICATIONS

3–5 years of experience in related field
Graph Databases: Experience with Neo4j or similar Graph DBs for advanced RAG (GraphRAG) and knowledge graph construction.
DevOps/MLOps: Experience with Docker, Kubernetes, or CI/CD pipelines for AI services.
Protocol Knowledge: Deep familiarity with JSON-RPC or the underlying architecture of MCP.
Knowledge of DBT.

Explore Our Benefits

e believe that anything can be realized. And we want to help you, make what you dream of come true. Become a part of our workforce!

Team Collaboration

We foster a culture of collaboration and innovation.

Career Development

We offer ongoing opportunities for your professional growth.

Experiencing Projects

Join the forefront of projects that present technological challenges.

Health & Insurance

We provide employee health and employment insurance. We are committed to maintaining employee welfare, one of which is by providing catering menus for every employee.