Basic Qualifications

• MS or PhD degree in Computer Science, Computer Engineering, Electrical Engineering or equivalent.
• 5+ years of NLP/Chatbot Machine learning development and deployment experience.
• Expertise in Python and ecosystem library pandas, spark, dask, machine learning/deep learning framework - pytorch, huggingface, scikit-learn, spacy, cupy ...etc.
• Strong ability in designing conversational AI Chatbot and dialogue management system integration.
• Strong ability in problem solving. Capable to reach out for solutions and enjoy closely collaborating with PM/engineers to share AI Chatbot design and solution ideas.
• Strong verbal and written communications skills are a must, as well as the ability to work effectively across internal and external organizations and virtual teams.

Preferred Qualifications

• Experience with Chatbot Platform. e.g. AWS Lex/Lambda/Kendra platforms or GCP Dialogflow/Cloud Function or RASA open-source chatbot frameworks.
• Experience fine-tuning pre-trained language models in specific domains. such as Transformer, BERT, GPTJ, GPT-NeoX, OpenAI-GPT.
• Experience with handling and analysis of large-scale feature extraction, similarity comparison, pattern recognition, clustering, text mining, natural language processing(NLP).
• Familiar with container model deployment in cloud platforms. e.g AWS ECS/Fargate/EKS, GCP CloudRun/GKE.
• Familiar with knowledge distillation like teacher-student learning paradigm.
• Familiar with passage/information retrieval and contrastive learning.
• Familiar with sequence to sequence learning and controllable sequence generation.

Job Description

• Design and Develop AI Chatbot services and target to make“human-like”conversation in dialogue management systems.
• Participate in the end-to-end machine/deep learning product lifecycle, from data collection, data exploration, feature engineering, model training, model evaluation, model deployment, model inference monitoring to solve real world cases.
• Arrange and design the training dataset, prompts and pipeline required for fine-tuning large language models and validation purposes.
• Design and establish ETL processes, system architecture, algorithm pipeline, and evaluation metrics for various NLP tasks.
• Investigate and implement robust approaches of slot filling, NLU, NLI/entailment, intent classification, text generation, question answering, and keyword extraction.
• Conduct effective training and automated inference/retrain pipelines.
• Stay up to date in current technologies and state of the art methods.
• Thrive in an agile development process and constructive feedback. Critical thinker, problem solver, team player, time management skills, good interpersonal and communication skills.