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Foodics is a leading restaurant management ecosystem and payment tech provider founded in 2014, headquartered in Riyadh with offices across 5 countries (UAE, Egypt, Jordan, Kuwait) and serving customers in over 35 countries. The company has processed over 6 billion orders and raised $170 million in the largest SaaS funding round in MENA, positioning it as one of the most rapidly evolving SaaS companies from the region.
In this role, you will lead the design, development, and deployment of ML/AI/GenAI models that power core Foodics products including pricing optimization, personalization engines, and fraud detection systems. You'll own the complete ML model lifecycle from problem framing through production deployment and ongoing monitoring.
Key responsibilities include:
- End-to-end ownership of ML model development: problem framing, data exploration, training, deployment, and monitoring
- Design and development of scalable solutions using classical ML and GenAI techniques
- Implementation of MLOps best practices including versioning, reproducibility, monitoring, and CI/CD for models
- Collaboration with Data Engineers, Product Managers, and Platform teams to deliver production-grade models with measurable business impact
- Mentoring of junior ML engineers and contribution to internal ML knowledge base
- Integration of models with APIs and backend services
- Adoption of "you build it, you run it" philosophy, owning the full lifecycle from development through monitoring and continuous improvement
REQUIREMENTS:
- 5+ years of applied ML, AI, or data science experience
- Strong Python proficiency and expertise with ML/AI libraries (scikit-learn, PyTorch, TensorFlow, XGBoost, HuggingFace Transformers)
- Experience with MLOps tools (MLflow, SageMaker) and managing versioning, testing, and observability
- Deep understanding of model development workflows: feature engineering, hyperparameter tuning, model evaluation, A/B testing
- Strong statistical modeling and inference knowledge with ability to apply statistical tests (t-test, chi-square, ANOVA, regression analysis) and communicate results to technical and non-technical audiences
- Proven track record of deploying ML models in production at scale
- Knowledge of ML best practices including bias mitigation, explainability (SHAP, LIME), and model monitoring for drift and fairness
- Strong understanding of data pipelines, experimentation frameworks, and model evaluation methodologies
- Familiarity with cloud-native environments (AWS preferred) including CI/CD, GitOps, and Infrastructure as Code tools (Terraform, CDK)
- Hands-on experience with GenAI/LLM integration including RAG, fine-tuning, embeddings, prompt engineering, and tools such as LangChain, LangGraph, or LlamaIndex
