Our client, a cutting-edge deep-tech AI platform in the MENA region, is seeking a Senior / Lead Machine Learning Engineer.
This role requires relocation to Riyadh, KSA
As a Senior 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.
This role involves end-to-end project ownership, from data preprocessing to the creation of service APIs, and offers opportunities to work on cutting-edge AI technologies.
You will be responsible for
- Mentoring junior team members, sharing knowledge, and advising on the best machine learning and software engineering practices and approaches.
- Establishing and maintaining robust communication channels with other cross-functional teams to facilitate the integration of machine learning solutions into other Unifonic products.
- Developing and optimizing 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, as well as AI trends (i.e., Generative AI).
- Collaborating with the data engineering team and other teams to collect and analyze 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 that should handle thousands of requests per second and help perform the required load tests to meet the SLA.
The Must-Haves
- 5+ years of relevant work experience as a Machine Learning Engineer.
- 3+ years of experience with Python.
- Excellent analytical abilities, with the capacity to collect, organize, and analyze 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, 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.
- Mastering data science requires skills like SQL, hypothesis testing, Data cleansing, data augmentation, data pre-processing techniques, and dimensionality reduction.
- Excellent understanding of Machine learning techniques like Naive Bayes classifiers, SVM,
- Decision Tree, KNN, K-means, Random Forest, modelling and optimization, evaluation metrics, classification, and clustering.
- Experience with LLM frameworks (i.e. LangChain) and prompt engineering techniques.
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