عالم بيانات أول / مهندس تعلم آلي أول (Senior Data Scientist / Senior ML Engineer)

Job Summary

This role involves leading the design, development, and deployment of machine learning and AI models that support core products at Foodics. The position requires collaboration with various teams to deliver production-ready models with significant impact.

Responsibilities

• Manage the entire ML model lifecycle including problem definition, data analysis, training, deployment, and monitoring.
• Create scalable solutions using classical machine learning and Generative AI methods.
• Apply MLOps best practices such as version control, reproducibility, monitoring, and CI/CD for models.
• Work closely with squads and platform teams to ensure model reusability and compliance with standards.
• Guide junior ML engineers and contribute to the internal knowledge base on machine learning.
• Integrate machine learning models with APIs and backend systems as required.
• Take full ownership of ML models from development through ongoing monitoring and enhancement.

Requirements

• More than 5 years of experience in applied machine learning, AI, or data science.
• Strong skills in Python and ML/AI libraries such as scikit-learn, PyTorch, TensorFlow, XGBoost, and HuggingFace Transformers.
• Experience with MLOps tools like MLflow and SageMaker, including versioning, testing, and observability.
• In-depth knowledge of model development workflows including feature engineering, hyperparameter tuning, model evaluation, and A/B testing.
• Strong understanding of statistical modeling, inference, and statistical tests with ability to communicate results to technical and non-technical audiences.
• Proven experience deploying machine learning models at scale in production environments.
• Knowledge of ML best practices including bias mitigation, explainability tools such as SHAP and LIME, and monitoring for model drift and fairness.
• Good understanding of data pipelines, experimentation, and model evaluation.
• Familiarity with cloud-native environments, preferably AWS, and tools like CI/CD, GitOps, Terraform, and CDK.
• Hands-on experience with Generative AI and large language model integration using techniques like RAG, fine-tuning, embeddings, prompt engineering, and tools such as LangChain, LangGraph, or LlamaIndex.

What the company offers

• Competitive compensation packages including bonuses and potential shares.
• An inclusive and diverse culture that fosters innovation.
• Focus on personal development with regular training and an annual learning stipend.
• Opportunity to work with a diverse team from over 30 nationalities across 14 countries.
• Autonomy, mentoring, and challenging goals that provide growth opportunities.

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