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Mid-level ML role, metro location, and broad skillset increase candidate competition.
Specialized ML, LLM and MLOps requirements moderately limit cross-industry fit.
Explicit years plus extensive mandatory ML, cloud, LLM and MLOps skills make screening strict.
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Design, develop, deploy, and optimize scalable AI/ML models and generative AI solutions for enterprise applications.
Build and maintain end-to-end machine learning pipelines including data ingestion, model training, evaluation, deployment, and monitoring on cloud platforms.
Implement MLOps best practices such as model versioning, monitoring, CI/CD automation, and collaborate cross-functionally to deliver AI-driven solutions.
3–11 years of professional experience in AI, Machine Learning, or Data Science.
Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or related field.
Hands-on experience with cloud AI platforms: GCP Vertex AI or Azure Machine Learning or AWS SageMaker.
Location requirement: Riyadh (Onsite).
Experienced with generative AI and large language models, including frameworks like Hugging Face and LangChain.
Proficient in Python and ML frameworks such as TensorFlow or PyTorch with practical knowledge of MLOps tools like Docker and Kubernetes.
Skilled in building cloud-native AI/ML architectures leveraging cloud services and data engineering tools like BigQuery ML, Dataflow, and Databricks.