AI Agent Algorithm Engineer
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.
- 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.
More Info
Key Skills
embedding fine-tuning
tool calling
recall ranking
fine-tuning LLMs via SFT and DPO
RAG retrieval systems
multi-agent LLM systems
task planning
About Company
Skills:
Python, embedding fine-tuning, SFT, owl, DPO, RAG retrieval systems, task planning, AutoGen, LangGraph, recall ranking, Agent frameworks, orchestration tool calling, multi-agent LLM systems, fine-tuning LLMs, Google Agent Development Kit
Skills:
Machine Learning, Python, Algorithms, Nlp, Agent frameworks, e-commerce guided shopping, Memory Agentic RL, search recommendation Copilots, Agentic Search, search recommendation, intelligent shopping assistants, Cost Optimization, LLM applications, Agentic SFT RL training, LLM Eval, context management, LangGraph, Harness Engineering, stability governance, Function Calling, Google ADK, Agent orchestration, OpenAI Agents SDK, latency optimization, workflow orchestration