Lead Machine Learning Engineer

2 days ago

Greater Toronto Area Canada, Ontario Talent Seed Full time

*Riyadh Based* - This role requires relocation to Riyadh, KSA


We seek a highly skilled and motivated Senior ML/AI Engineer with a deep understanding of Large Language Models (LLMs) to join our growing AI team. This role will develop and refine advanced machine-learning models to enhance customer interactions and engagement. By leveraging LLMs, the engineer will optimize conversational AI systems, personalize user experiences, and implement intelligent solutions that drive customer satisfaction. Their expertise in machine learning, natural language processing, and deep understanding of customer experience strategies will be instrumental in advancing our CX capabilities.


Qualifications:

  • Expertise in Large Language Models (LLMs):
  • Proficiency in developing, fine-tuning, and optimising proprietary or open-source LLMs to enhance customer experience applications.
  • Strong Programming Skills: Advanced knowledge of programming languages such as Python, along with experience using machine learning frameworks and libraries like TensorFlow, PyTorch, or Hugging Face Transformers.
  • Experience in Natural Language Processing (NLP): Deep understanding of NLP techniques, including text analysis, sentiment detection, conversational AI, and dialogue management.
  • Knowledge of Customer Experience (CX) Strategies: Ability to align machine learning solutions with CX goals, leveraging data to personalize interactions and improve overall customer engagement and satisfaction.


Nice to have:

  • Expertise in Machine Learning Algorithms: Proficiency in developing and optimizing machine learning models, including experience with algorithms relevant to credit risk assessment.
  • Knowledge of Credit Risk Analysis: Deep understanding of credit risk methodologies, financial risk modelling, and industry regulations impacting credit evaluations.
  • Experience with Data Analysis and Big Data Tools: Ability to work with large financial datasets using tools like SQL, Hadoop, or Spark for data extraction and processing.

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