AI Engineer – LLM Algorithm Engineer (Agentic Commerce)

SHOPEE IP SINGAPORE PRIVATE LIMITED; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

SHOPEE IP SINGAPORE PRIVATE LIMITED is seeking an experienced ML/LLM engineer to lead continuous pre-training and post-training of large language models across vertical domains. You will design autonomous agents for data extraction and synthesis, and develop RL-based post-training methods to boost domain QA and reasoning capabilities.

You will also build multimodal LLM applications for content generation and recall, and craft end-to-end agent workflows that integrate with downstream product and

Qualifications

• Master's degree or above in CS/NLP/AI or related field.
• Minimum 5 years of full-time industry experience in LLM/ML engineering.
• Hands-on experience building autonomous agent systems for knowledge retrieval.
• Experience with LLM post-training techniques (SFT, RL-based methods).
• Experience fine-tuning and deploying multimodal large models.
• Strong programming skills and solid ML fundamentals.

Responsibilities

• Lead continuous pre-training and post-training of large language models for vertical domains.
• Design and build autonomous agents to retrieve and synthesize domain knowledge.
• Develop RL-based post-training methods to improve QA and reasoning tasks.
• Build and optimize multimodal LLM applications for content generation and recall.
• Design end-to-end agent workflows integrating query understanding and systems.
• Build representation-learning pipelines for embeddings in ranking models.
• Collaborate across teams to translate requirements into scalable model training and deployment.

Skills

LLM/ML engineering

Autonomous agents

Multimodal models

Data synthesis

RL post-training

Production deployment

Problem solving

Cross-functional collaboration

Education

Master's degree or above in CS/NLP/AI/EE/ECE/Signal Processing

Tools

LangGraph

LightGBM

XGBoost

Bayesian modeling

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.