Senior Machine Learning Engineer

What you'll work on

• Proven experience designing and implementing RAG systems, including familiarity with various retrieval strategies (e.g., BM25, dense retrieval, hybrid approaches) and knowledge graph integration.
• Hands-on experience with LLM orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar tools for building and managing autonomous agents.
• Deep expertise in various NLP techniques and models, including but not limited to:Transformer architectures (e.g., BERT, GPT, T5, LLama, Mistral)Large Language Models (LLMs) and their fine-tuning/adaptationVector embeddings and similarity searchText classification, named entity recognition (NER), sentiment analysis, summarization, and question answering.
• Hands-on 3-5 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, 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 (SOLID principles), considering the best software engineering fundamentals, i.e. data structures, algorithms, and data modeling
• 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 needed skills like SQL, hypothesis testing, Data cleansing, data augmentation, data pre-processing techniques, and dimensionality reduction.
• Basic knowledge of Kubernetes and Docker is nice to have.
• Excellent understanding of Machine learning techniques like Naive Bayes classifiers, SVM, Decision Tree, KNN, K-means, Random Forest, modeling and optimization, evaluation metrics, classification, and clustering.
• Experience with the Hugging Face libraries (i.e. transformers).
• Experience fine-tuning pre-trained models and using vector search to enhance LLMs results.
• Experience with LLM frameworks (i.e. LangChain) and prompt engineering techniques.
• Familiar with code versioning tools such as GIT, CI/CD concepts, and toolchains.
• Familiar with Agile methodologies i.e. scrum and kanban.
• Ability to develop high-level architecture and low-level design, End-to-end for a specific project.
• Experience in event sourcing patterns and tools i.e. Kafka, RabbitMQ, or similar is a plus.
• Experience with LLM frameworks (i.e. LangChain) and prompt engineering techniques is nice to have.
• Experience in event sourcing patterns and tools i.e. Kafka, RabbitMQ, or similar is nice to have.
• General knowledge of Data warehouse tools e.g. Vertica is a plus.
• A Bachelor’s degree in a relevant field. (e.g. Computer Science, Computer Engineering, Software, etc).
• Excellent communication and collaboration skills.
• Good level of spoken and written Arabic and English.