AI Engineer

Fourtitude Asia; Petaling Jaya; On-site; MYR 54,000 - 71,000; Full time

Job summary

Fourtitude Asia in Malaysia seeks an entry-level AI Engineer to join our AI team. You’ll build LLM-powered applications, RAG pipelines, and autonomous agents for client projects across financial services, insurance, telecom, and more.

You will work hands-on with senior engineers to turn business problems into production-ready features, including data preparation, model evaluation, and deployment support, gaining real-world AI engineering experience.

Qualifications

• Diploma or Bachelor's in CS/Computing or related field.
• Academic foundation in ML, statistics, data structures, and algorithms.
• Proficiency in Python and working knowledge of SQL.
• Understanding of core ML concepts, training and evaluation.
• Genuine interest in LLMs, prompting, and building apps on top of them.
• Strong desire to learn AI engineering and agent frameworks.
• Analytical thinking and a systematic approach to AI problems.
• Good written and verbal communication for collaboration.

Responsibilities

• Develop LLM-powered applications and RAG pipelines.
• Integrate tools and APIs for autonomous agents.
• Collaborate with senior engineers and PMs.
• Prepare data and evaluate models.
• Assist deployment and monitoring of AI apps.

Skills

Python

SQL

LLM concepts

Problem solving

Communication

Learning mindset

Analytical thinking

Team collaboration

Education

BSc CS

Data Science

Tools

LangChain

OpenAI API

PyTorch

TensorFlow

OpenSearch

pgvector

FAISS

Docker

Git

PostgreSQL

MySQL

MongoDB

LangGraph

AWS

GCP

Azure

Job description

Role Overview: We're seeking a motivated entry-level AI Engineer to join our AI team. You’ll help build LLM-powered applications and autonomous AI agents that go into the customized software we deliver for clients across financial services, insurance, telecom, retail, and utilities.

The role is hands‑on, involving turning business problems into working AI features such as retrieval‑augmented applications, document‑processing systems, and multi‑step agents, and getting them into production alongside our senior engineers. It’s a strong opportunity for a recent graduate to build real AI engineering experience on live client projects.

Key Responsibilities

LLM Application Development

Prompt Engineering: Develop and iterate on prompting strategies for reliable, task‑specific outputs.

LLM Integration: Build applications on top of large language models for document processing, customer support automation, and financial analysis.

Retrieval‑Augmented Generation (RAG): Assist in building RAG pipelines which includes chunking, embeddings, vector search, and grounding responses in client data.

Evaluation & Guardrails: Help test LLM outputs for accuracy and safety, and apply basic guardrails such as PII handling, prompt‑injection checks, and output validation.

Agentic AI Development

Agent Development: Learn to build autonomous and multi‑agent systems that plan and execute multi‑step tasks.

Tool Integration: Create and integrate tools and APIs that agents use to interact with external systems.

Orchestration: Work with agent frameworks and orchestration tooling to compose reliable agent workflows.

Model Development: Assist in training, fine‑tuning, and optimizing machine learning models for classification, regression, and NLP tasks.

Model Evaluation: Support evaluation of model performance and help interpret results.

Data Preparation: Prepare and curate datasets for training and evaluation, including labeling, cleaning, and formatting.

AI Application Engineering

Python Development: Build Python applications, microservices, and APIs that serve AI models and agents.

Deployment Support: Support deployment of AI applications to production, including basic CI/CD and containerized workflows.

Monitoring: Help set up monitoring and alerting for model performance and application health.

Cross‑Functional Responsibilities

Research & Development: Keep current with AI/ML and agent developments; experiment with new models, frameworks, and techniques.

Collaboration: Work with senior engineers, product managers, and business stakeholders to identify and shape AI opportunities.

Documentation: Document prompts, agent designs, model architectures, and runbooks so others can build on your work.

Required Qualifications

Education: Diploma or Bachelor’s Degree in Computer Science, Computing, Software Engineering, Data Science, Artificial Intelligence, Mathematics, Statistics, or a related field. Recent graduates are welcomed.

Academic Foundation: Coursework in machine learning, statistics, data structures, and algorithms.

Programming Skills: Proficiency in Python and working knowledge of SQL, with academic or personal project experience.

ML Foundation: Understanding of core machine learning concepts and how models are trained and evaluated.

LLM Curiosity: Genuine interest in large language models, prompting, and building applications on top of them.

Learning Mindset: Strong desire to learn AI engineering and agent frameworks, with the adaptability to pick up new tools quickly.

Problem Solving: Analytical thinking and a systematic approach to AI and data problems.

Communication: Good written and verbal communication for collaborative work and stakeholder discussions.

Preferred Qualifications

LLM Experience: Personal projects, coursework, or internship experience building with LLMs (e.g. Anthropic, OpenAI, or open models).

Agent Development: Familiarity with agent frameworks such as Strands and LangChain, and orchestration with LangGraph.

RAG & Vector Search: Exposure to embeddings and vector databases such as OpenSearch, pgvector, or FAISS.

Deep Learning Frameworks: Academic projects using PyTorch or TensorFlow.

Data Science Libraries: Experience with Pandas and NumPy.

Cloud Exposure: Basic familiarity with AWS (preferred), GCP, or Azure, and with cloud AI services such as Amazon Bedrock.

Containers & Version Control: Working knowledge of Docker and Git.

Databases: Experience with SQL (PostgreSQL, MySQL) and NoSQL (MongoDB) databases.