Design, develop, train, deploy, and monitor ML and generative AI models and end-to-end pipelines. Build LLM-powered applications, optimize model performance, implement MLOps (versioning, CI/CD, monitoring), and collaborate with engineers and stakeholders to deliver scalable cloud-native AI solutions.
Job Description: AI / ML Engineer
Job Title: AI / ML Engineer
Experience: 3–11 Years
Location: Riyadh (Onsite)
Employment Type: Full-Time
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.
- 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.
- Strong proficiency in Python.
- Experience developing machine learning solutions using TensorFlow or PyTorch.
- Strong understanding of supervised, unsupervised, reinforcement learning, and deep learning concepts.
- 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.
- Experience with Databricks for data engineering, model development, and analytics workflows.
- Strong understanding of data preprocessing, feature engineering, and large-scale data processing.
- Experience deploying machine learning models into production.
- Knowledge of Docker, Kubernetes, CI/CD pipelines, and model monitoring is an advantage.
- 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.
- 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.
- 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)
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