AI Engineer – LLM Algorithm Engineer (Agentic Commerce)

amIT Global Solutions Sdn Bhd; Indonesia; On-site; IDR 450,000,000 - 750,000,000; Full time

Job summary

amIT Global Solutions Sdn Bhd in Indonesia seeks a senior ML/LLM engineer to lead end-to-end development of domain-specific large language models. The role emphasizes continuous pre-training, post-training, and the design of autonomous agents to extract and synthesize knowledge for downstream production systems.

You will collaborate across teams to deploy multimodal models, optimize agent workflows, and deliver measurable business impact through content generation, semantic search, and ranking

Qualifications

• Master's degree or higher in CS/NLP/AI or related field.
• Minimum 5 years of full-time industry experience in LLM/ML engineering with large-scale pre-training.

Responsibilities

• Lead continuous pre-training and post-training of large language models for vertical domains.
• Design and build autonomous agents to retrieve, extract, and synthesize domain knowledge.
• Develop RL-based post-training methods to improve QA, reasoning and knowledge modeling.
• Build multimodal LLM applications for content generation and semantic recall.
• Design end-to-end agent workflows integrating query understanding and downstream systems.
• Create generative representation-learning pipelines for downstream ranking and recommendations.
• Collaborate cross-functionally to translate requirements into scalable model training and agent design.

Skills

LLM/ML engineering

Autonomous agents

Multimodal models

Production deployment

Problem solving

Education

Master's degree or above in CS/AI

Tools

LangGraph

Python

PyTorch

TensorFlow

Job description

Job Description:

• Lead continuous pre-training and post-training of large language models (Dense and MoE architectures) for vertical business domains, including domain-specific data synthesis, multi-stage data curation, and monthly iteration to support downstream business integration.
• Design and build autonomous agents (e.g., Plan-and-Execute agents) that automatically retrieve, extract, and synthesize domain knowledge from large-scale multilingual/unstructured data sources, producing high-confidence corpora for continued pre-training.
• Develop and iterate RL-based post-training methods (e.g., CPT, SFT, DAPO) to improve model performance on domain-specific QA, reasoning, and knowledge-modeling tasks.
• Build and optimize multimodal LLM applications for content generation and understanding, including product copywriting, semantic search recall, and business potential/CTR prediction.
• Design end-to-end agent workflows integrating query understanding, content generation, validation, and downstream product/search systems.
• Build generative representation-learning pipelines (e.g., token-based generative pre-training on behavioral sequences) to produce reusable embeddings for downstream ranking and recommendation models.
• Collaborate cross-functionally to translate business requirements into scalable model training and agent system design continuously monitor production metrics to guide model and agent optimization.

Requirements:

• Master's degree or above in Computer Science, Natural Language Processing, Artificial Intelligence, Electrical and Electronics Engineering, Signal Processing or a related field.
• Minimum 5 years of full-time industry experience in LLM/ML algorithm engineering, including hands-on experience with large-scale model continuous pre-training (Dense and/or MoE architecture) for vertical business domains.
• Hands-on experience designing and building autonomous agent systems (e.g., using LangGraph or similar frameworks) for automated knowledge retrieval, extraction, and synthesis pipelines, combined with hands-on experience in LLM post-training techniques (SFT, RL-based methods such as DAPO) and data-mixture optimization techniques for pre-training data curation.
• Experience fine-tuning and deploying multimodal large models for content generation, semantic recall, or business potential prediction, with demonstrated production impact on key business metrics (e.g., CTR, conversion).
• Good programming and engineering skills solid foundation in classic ML techniques (e.g., LightGBM, XGBoost, Bayesian modeling) is a plus.
• Good problem-solving skills able to independently drive projects from research through to large-scale production deployment.
• Prior experience in multimodal retrieval/recommendation systems (e.g., cross-modal contrastive learning for content matching) will be a strong plus.