AI Agent Algorithm Engineer

SHOPEE IP SINGAPORE PRIVATE LIMITED; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

SHOPEE IP SINGAPORE PRIVATE LIMITED is hiring for a senior AI/ML role focused on building production-grade multi-agent LLM systems. Responsibilities include planning and orchestration, tool calling, RAG, and memory management across long context interactions.

The candidate will lead pre-training/post-training efforts for domain-specific scenarios, develop data pipelines, and design rigorous evaluation plans to guide agent optimization and product impact.

Qualifications

• Master's degree or above in Artificial Intelligence, Computer Science, Mathematics, or related field.
• At least 2 years of full-time industry experience building and deploying production multi-agent LLM systems.

Responsibilities

• Build core Agent logic: planning, orchestration, tool calling, multimodal RAG and memory.
• Lead data pipelines, RL reward models and improved instruction following for dialogue agents.
• Develop long-term memory architectures to maintain coherence in long interactions.
• Design and optimize prompts and context management for product-driven requirements.
• Evaluate agent performance with rigorous quantitative plans and metrics.

Skills

Python

Multi-agent LLMs

Problem solving

Collaboration

Education

Master's degree

Tools

LangGraph

Google Agent Development Kit

OWL

AutoGen

Job description

Job Description:

• Build core Agent logic, including but not limited to task planning and orchestration, tool calling, multi-turn dialogue management, memory, RAG, context engineering, and multi-agent collaboration.

• Lead Continuous Pre-training and Post-training for vertical domains and business scenarios, including building high-quality datasets and data pipelines, designing RL reward models, improving instruction following and reasoning capabilities, task completion, role-playing, anthropomorphic and personalized dialogue, proactive/reactive immersive multimodal conversation experiences, and enhancing the model's IQ and EQ.

• Build long-term and short-term memory architectures, addressing issues such as forgetting and attention dispersion in long contexts, and improving immersion and consistency in long-term user interactions.

• Build multimodal RAG systems, including development and optimization of key modules such as recall, ranking, long-text processing, and multi-document synthesis.

• Develop the Agent's tool layer, integrating external APIs and MCP such as search, code interpreters, browsers, sandboxes, and third-party services.

• Design and tune prompts and context management, with tailored optimization for different product requirements.

• Design scientifically rigorous quantitative evaluation systems and plans aligned with product requirements; continuously monitor product metrics and provide guidance for Agent and model optimization.

• Explore innovative AI applications.

Requirements:

• Master's degree or above in Artificial Intelligence, Computer Science, Mathematics, or a related field.

• At least 2 years of full-time industry experience building and deploying production multi-agent LLM systems (task planning, orchestration, tool calling).

• Hands-on experience fine-tuning LLMs via SFT and DPO, combined with hands-on experience building and optimizing RAG/retrieval systems (recall, ranking, embedding fine-tuning).

• Good programming skills; proficient in Python

• Good problem solving analysis and resolution skills; sustained interest and curiosity in frontier AI technologies and applications; strong self-drive; able to collaborate closely with teams to drive a full closed loop from research to deployment.

• Good development experience with Agent frameworks such as LangGraph, Google Agent Development Kit, OWL, or AutoGen.