Huawei Canada has an immediate 12-month contract opening for an Engineer.

About the team:

Established in 2014, the Distributed Scheduling and Data Engine Lab is Huawei Cloud's technical innovation center in Canada. The lab focuses on researching and developing advanced cloud technologies, supporting the productization and iterative optimization of its technical achievements. Current research areas include cloud native databases, infrastructure resource scheduling and prediction, cloud-native middleware, media engines, and user experience studies. The lab fosters a robust technical environment, allowing collaboration with industry experts to create a highly competitive cloud platform.

About the job:

• Research & Build (and/or Prototype) Gen-AI features using LLMs.

• LLM customization through techniques such as prompt engineering, RAG, and fine-tuning.

• Collaborate with UX designer and front-end developer to incorporate UI design to the Gen-AI platform.

• Collaborate with platform software engineers for integrating Gen-AI features into existing solution.

• Conduct technical research and present to the team.

Requirements

About the ideal candidate:

• Strong background in mathematics and statistics & strong foundation in algorithms, data structure, and object-oriented-programming along with proficiency in Python, R, and Java.

• Experience in using ML and exploratory data analysis (EDA) tools and libraries including Numpy, Pandas, Matplotlib, Seaborn, and Scikit-learn.

• In-depth knowledge of Artificial neural networks like CNN, RNN, LSTM, and GRU, along with expertise in DL frameworks such as PyTorch, TensorFlow and JAX.

• Deep theoretical knowledge of advanced NLP and Gen AI concepts including transformers, pre-training and transfer learning, along with experience in fine-tuning Auto-encoder, Autoregressive and Seq2Seq Language Models from Hugging Face.

• Expertise in cutting-edge Gen AI methodologies, including agentic AI, multi-agent systems, graph RAG, reasoning LLMs, and agent-building frameworks such as LangChain and LlamaIndex.

• Knowledge of classical and modern Reinforcement Learning techniques, including natural langugae-based RL, RLHF, and preference alignment.

• Experience in AI-driven product development using multimodality.