AI/Machine Learning Application Engineer
Location: Shanghai, China
About Us:
Pacvue is the leading software suite for eCommerce advertising, sales, and intelligence. We help some of the world’s largest brands grow their business on Amazon, Walmart, Instacart, and other marketplaces and work with sellers and agencies of all sizes to help them compete in the constantly changing world of online retail. Our mission is to empower teams to win in the future of eCommerce, and we do it by building first-to-market technology, solving complex problems with our customers, and bringing expertise, collaboration, and innovation to our work every single day.
Why work at Pacvue?
Be on the cutting edge - Pacvue is transforming the way brands and sellers win online. Our product uses machine learning, artificial intelligence, and data to make intelligent decisions and recommendations.
Have fun – we have an energetic and passionate team with a joint mission to win and help our brands and sellers succeed.
Learn – from the best! Our team is full of talented people who want to help you learn, grow – providing you with mentorship, the industry’s best practices and thought leadership.
Grow fast – the eCommerce industry has grown fast in the past 2-3 years. Pacvue has grown even faster than most high-tech companies in the market.
About the Role
We are looking for an AI/ML Application Engineer with solid foundations in machine learning and deep learning, and proven experience applying LLM to real problems. The ideal candidate combines strong technical understanding of ML algorithms with hands-on experience in building and deploying AI applications, retrieval-augmented generation (RAG), and agentic AI frameworks. You will play a key role in designing, implementing, and optimizing intelligent systems for advertising solutions that integrate machine learning pipelines with generative AI capabilities to drive business impact.
Responsibilities
Design and develop AI-powered applications leveraging machine learning, RAG, and agentic architectures.
Build end-to-end AI/ML pipelines for data preprocessing, model training, evaluation, and deployment.
Integrate LLMs with retrieval systems and domain knowledge bases to create context-aware applications.
Conduct model fine-tuning, optimization, and distillation to enhance performance and reduce inference cost.
Collaborate with product and engineering teams to transform business needs into scalable AI solutions.
Monitor, evaluate, and continuously improve deployed models based on real feedback and performance metrics.
Skills and Qualifications
You have a solid foundation in machine learning and deep learning, and you're comfortable building models with PyTorch or TensorFlow.
You love coding in Python and know your way around libraries like NumPy, pandas, and scikit-learn.
You've built or experimented with LLM applications using LangChain, LlamaIndex, or Haystack, and know how to combine retrieval and generation to make AI truly useful.
You understand how vector search and semantic retrieval work (think FAISS, Elasticsearch, or Milvus).
You're familiar with prompt engineering, chain-of-thought reasoning, and how to make agents "think" and "remember."
You've worked on real NLP problems — cleaning messy text, extracting meaning, and comparing semantic similarity.
Bonus points: if you've played with model fine-tuning techniques like LoRA, P-Tuning, or Adapters, and know how to squeeze performance out of large models efficiently.
Bonus points: if you've touched MLOps tools (like MLflow, Docker, or Airflow) or deployed models on cloud platforms (AWS, GCP, Azure).
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