You will lead the design, development, and deployment of ML/AI/GenAI models that power core the company's products (e.g., pricing, personalization, fraud detection). You’ll collaborate with Data Engineers, Product Managers, and Platform teams to deliver production-grade models with real impact.
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What Will You Do
- Own end-to-end ML model lifecycle: problem framing, data exploration, training, deployment, monitoring.
- Design and develop scalable solutions using classical ML and GenAI techniques.
- Implement MLOps best practices: versioning, reproducibility, monitoring, CI/CD for models.
- Collaborate with squads and platform teams to ensure reusability and adherence to standards.
- Mentor junior ML engineers and contribute to the internal ML knowledge base.
- Integrate models with APIs and backend services as needed.
- Embrace and enforce a "you build it, you run it" approach, owning the full lifecycle of ML models from development through monitoring and continuous improvement.
Location
Riyadh, Saudi Arabia
Requirements
- 5+ years’ experience in applied ML, AI, or data science.
- Strong proficiency in Python and ML/AI libraries (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, HuggingFace Transformers).
- Experience with MLOps tools (e.g., MLflow, SageMaker) and managing versioning, testing, and observability.
- Deep understanding of model development workflows including feature engineering, hyperparameter tuning, model evaluation, and A/B testing.
- Deep understanding of statistical modeling, statistical inference, and the appropriate application of statistical tests (e.g., t-test, chi-square, ANOVA, regression analysis); ability to interpret results and communicate implications to both 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 (e.g., SHAP, LIME), and model monitoring for drift and fairness.
- Strong understanding of data pipelines, experimentation, and model evaluation.
- Familiarity working in a cloud-native environment (AWS preferred) with CI/CD, GitOps, and IaC tools (e.g., Terraform, CDK).
- Hands-on experience with GenAI / LLM integration (e.g., RAG, fine-tuning, embeddings, prompt engineering) and tools such as LangChain, LangGraph, or LlamaIndex.
