Principal LLM Engineer

Srkay Consulting Group; Kuala Lumpur; On-site; MYR 120,000 - 180,000; Full time

Benefits offered by this job

EPF

SOCSO

Annual Leave

Medical Leave

Health Insurance

Job summary

Jora Malaysia is seeking an experienced LLM Engineer to design, develop, and deploy large language models and related AI systems at scale in Kuala Lumpur. You will build robust APIs and integrate LLM services with other software components, writing clean Python code for training, evaluation, and deployment pipelines.

You will collaborate with product managers, data scientists, and software engineers to define project requirements and deliver innovative solutions, staying current with NLP

Qualifications

• Design, develop, and deploy large language models (LLMs) and related AI systems, ensuring scalability and efficiency.
• Implement and fine-tune state-of-the-art LLM architectures for NLP tasks (text generation, summarization, translation, QA).
• Develop robust APIs and integration points to enable interaction between LLM services and other software components.
• Write clean, efficient Python code for model training, evaluation, and deployment pipelines.
• Collaborate with product managers, data scientists, and software engineers to define project requirements and deliver solutions.
• Stay abreast of latest LLM/NLP advancements and apply knowledge to improve systems and explore opportunities.
• Troubleshoot and debug complex issues in LLM models and infrastructure.
• Contribute to development of best practices for LLM engineering.

Responsibilities

• Design, develop, and implement advanced Large Language Models (LLMs) and related AI systems to solve complex business problems.
• Collaborate with cross-functional teams to define LLM project requirements and deliverables.
• Fine-tune and optimize LLM architectures for specific tasks, ensuring performance and scalability.
• Develop and maintain robust APIs for integrating LLM capabilities into products and services.
• Conduct research and stay updated on NLP and ML advancements.
• Write clean Python code for model development, training, and deployment.
• Evaluate and benchmark LLM performance and implement improvements.
• Mentor engineers and share knowledge on LLM deployment strategies.

Skills

Python

LLM/NLP concepts

Cloud platforms

MLOps practices

API development

Debugging

Cross-functional collaboration

Tools

AWS

Azure

GCP

CI/CD pipelines

Job description

Jora Malaysia will close on 9th September 2026. Thank you for being with us, we are cheering you on as you continue your career journey.

Design, develop, and deploy large language models (LLMs) and related AI systems, ensuring scalability and efficiency.

Implement and fine-tune state-of-the-art LLM architectures for various natural language processing tasks, including text generation, summarization, translation, and question answering.

Develop robust APIs and integration points to enable seamless interaction between LLM services and other software components.

Write clean, efficient, and well-documented Python code for model training, evaluation, and deployment pipelines.

Collaborate with cross-functional teams, including product managers, data scientists, and software engineers, to define project requirements and deliver innovative solutions.

Stay abreast of the latest research and advancements in LLM technology, NLP, and machine learning, and apply this knowledge to improve existing systems and explore new opportunities.

Troubleshoot and debug complex issues in LLM models and associated infrastructure, ensuring high availability and performance.

Contribute to the development of best practices and standards for LLM engineering within the organization.

Experience with cloud platforms (e.g., AWS, Azure, GCP) for deploying and managing large-scale AI models.

Familiarity with MLOps principles and tools for model lifecycle management, monitoring, and continuous integration/continuous deployment (CI/CD).

Requirement

• Design, develop, and deploy large language models (LLMs) and related AI systems, ensuring scalability and efficiency.

• Implement and fine-tune state-of-the-art LLM architectures for various natural language processing tasks, including text generation, summarization, translation, and question answering.

• Develop robust APIs and integration points to enable seamless interaction between LLM services and other software components.

• Write clean, efficient, and well-documented Python code for model training, evaluation, and deployment pipelines.

• Collaborate with cross-functional teams, including product managers, data scientists, and software engineers, to define project requirements and deliver innovative solutions.

• Stay abreast of the latest research and advancements in LLM technology, NLP, and machine learning, and apply this knowledge to improve existing systems and explore new opportunities.

• Troubleshoot and debug complex issues in LLM models and associated infrastructure, ensuring high availability and performance.

• Contribute to the development of best practices and standards for LLM engineering within the organization.

• Experience with cloud platforms (e.g., AWS, Azure, GCP) for deploying and managing large-scale AI models.

• Familiarity with MLOps principles and tools for model lifecycle management, monitoring, and continuous integration/continuous deployment (CI/CD).

Responsibility

• Design, develop, and implement advanced Large Language Models (LLMs) and related AI systems to solve complex business problems.

• Collaborate with cross-functional teams, including product managers and data scientists, to define LLM project requirements and deliverables.

• Fine-tune and optimize existing LLM architectures for specific tasks, ensuring performance, scalability, and efficiency.

• Develop and maintain robust APIs for integrating LLM capabilities into existing and new products and services.

• Conduct research and stay abreast of the latest advancements in LLM technology, natural language processing (NLP), and machine learning.

• Write clean, efficient, and well-documented Python code for model development, training, and deployment.

• Evaluate and benchmark LLM performance, identifying areas for improvement and implementing solutions.

• Contribute to the development of best practices and standards for LLM engineering within the organization.

• Troubleshoot and debug issues related to LLM models and their integration into production environments.

• Mentor junior engineers and share knowledge on LLM development and deployment strategies.

Benefits

• EPF
• SOCSO
• Annual Leave
• Medical Leave
• Health Insurance