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Lead Machine Learning Engineer Riyadh

Talent Seed
Toronto, ON
Posted 10 days ago
Job Details:
Full-time
Experienced

Riyadh Based - This role requires relocation to Riyadh, KSA

As a Lead Machine Learning (AI) Engineer, you will design, develop, and deploy advanced machine learning solutions across various domains, including NLP, LLMs, Recommender engines, and anomaly detection.

  • Mentoring junior team members, sharing knowledge, and advising on the best machine learning and software engineering practices and approaches.
  • Developing and optimising highly confident machine learning algorithms and models, and creating/exposing the service APIs using frameworks such as Flask, FastAPIs, or other relevant frameworks.
  • Staying up-to-date with the latest machine learning research papers and AI trends (i.e. Generative AI).
  • Collaborating with the data engineering team and other teams to collect and analyse extensive datasets, extracting insights and patterns in real-time, near-real-time, or batch processing mode.
  • Implementing proof of concepts and prototypes to demonstrate the potential of new AI use cases and innovations.
  • Building scalable, maintainable machine learning services, which should handle thousands of requests per second, and help to perform the required load tests to meet the SLA.

Required Qualifications:

  • Hands-on 5+ years of relevant work experience as a Machine Learning Engineer.
  • Hands-on 3+ years of experience with Python.
  • Excellent analytical abilities, with the capacity to collect, organise, and analyse large datasets to glean valuable insights.
  • End-to-end experience in training, evaluating, testing, and deploying machine learning products in production.
  • Ability to write world-class code in Python (SOLID principles), considering the best software engineering fundamentals, i.e. data structures, algorithms, and data modelling
  • Solid experience in ML frameworks such as NumPy, Pandas, Scikit-Learn, PyTorch, Keras, BERT, Tensorflow, and similar.
  • Familiarity with MLOps best practices, e.g. Model deployment and reproducible research.
This position is no longer available.

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