AI / ML Engineer

Job Description: AI / ML Engineer Job Title: AI / ML Engineer Experience: 3 11 Years Location: Riyadh (Onsite) Employment Type: Full-Time Job Overview We are seeking a skilled AI / ML Engineer with 3 11 years of experience to design, develop, deploy, and optimize machine learning and generative AI solutions. The ideal candidate will have hands-on expertise in building scalable AI/ML models, working with cloud-native AI platforms, and implementing production-ready machine learning pipelines. Experience with modern AI frameworks, large language models (LLMs), and MLOps practices is highly desirable. Key Responsibilities Design, develop, train, and deploy machine learning and deep learning models for enterprise applications. Build and optimize end-to-end ML pipelines for data ingestion, model training, evaluation, and deployment. Develop Generative AI and LLM-powered applications using modern AI frameworks. Collaborate with data engineers, software developers, and business stakeholders to deliver AI-driven solutions. Deploy and monitor ML models on cloud platforms while ensuring scalability, reliability, and security. Optimize model performance through feature engineering, hyperparameter tuning, and continuous evaluation. Implement MLOps best practices including model versioning, monitoring, and CI/CD automation. Stay current with advancements in AI, machine learning, and cloud AI services. Required Technical Skills Cloud AI Platforms Hands-on experience with GCP Vertex AI or Azure Machine Learning or AWS SageMaker . Experience with Azure OpenAI or AWS Bedrock for Generative AI solutions. Experience with BigQuery ML and Dataflow for data processing and machine learning workflows. Programming & Machine Learning Strong proficiency in Python . Experience developing machine learning solutions using TensorFlow or PyTorch . Strong understanding of supervised, unsupervised, reinforcement learning, and deep learning concepts. Generative AI & LLM Frameworks Experience with Hugging Face and LangChain for building LLM-powered applications. Knowledge of prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, and vector databases is preferred. Data Engineering & Analytics Experience with Databricks for data engineering, model development, and analytics workflows. Strong understanding of data preprocessing, feature engineering, and large-scale data processing. MLOps & Deployment Experience deploying machine learning models into production. Knowledge of Docker, Kubernetes, CI/CD pipelines, and model monitoring is an advantage. Qualifications Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field. 3 11 years of professional experience in AI, Machine Learning, or Data Science. Strong analytical, mathematical, and problem-solving skills. Experience working in Agile development environments. Excellent communication and collaboration skills. Preferred Skills Experience with Large Language Models (LLMs) and Generative AI applications. Knowledge of Retrieval-Augmented Generation (RAG), vector databases, and AI agents. Experience with distributed model training and cloud-native AI architectures. Cloud certifications in AWS, Azure, or Google Cloud are a plus. Key Technology Stack Cloud AI: GCP Vertex AI or Azure Machine Learning or AWS SageMaker Generative AI: Azure OpenAI or AWS Bedrock and Large Language Models (LLMs) Data Processing: BigQuery ML and Dataflow and Databricks Programming: Python Machine Learning Frameworks: TensorFlow or PyTorch LLM Frameworks: Hugging Face or LangChain MLOps: Docker and Kubernetes and CI/CD (Preferred) Share

Qualifications

Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.

3 11 years of professional experience in AI, Machine Learning, or Data Science.

Strong analytical, mathematical, and problem-solving skills.

Experience working in Agile development environments.

Excellent communication and collaboration skills.

Preferred Skills

Experience with Large Language Models (LLMs) and Generative AI applications.

Knowledge of Retrieval-Augmented Generation (RAG), vector databases, and AI agents.

Experience with distributed model training and cloud-native AI architectures.

Cloud certifications in AWS, Azure, or Google Cloud are a plus.