Machine Learning Engineer

About the Role

We are seeking a seasoned Machine Learning Engineer with between 3 and 7 years of experience to join our team in Abu Dhabi Emirate, United Arab Emirates. This role involves designing, developing, and deploying advanced machine learning and AI models to solve complex business challenges.

Key Responsibilities

• Design, train, and refine machine learning, deep learning, and artificial intelligence models.
• Create scalable, end-to-end machine learning pipelines for efficient data processing and model deployment.
• Engage in fine-tuning of Large Language Models (LLMs) and Small Language Models (SLMs), as well as developing complex AI system architectures.
• Develop and deploy Agentic AI solutions that can operate autonomously.
• Build APIs using frameworks such as FastAPI and Flask for model serving and integration.
• Automate machine learning workflows using continuous integration/continuous deployment (CI/CD) and MLOps methodologies.
• Deploy and optimize models for both real-time and batch inference scenarios.
• Work with containerization and orchestration tools including Docker and Kubernetes.
• Utilize machine learning lifecycle tools like MLflow and Kubeflow for model tracking and management.
• Deploy AI and machine learning solutions on cloud platforms, specifically AWS or Azure.
• Collaborate effectively across multiple teams to deliver production-ready AI applications.

Required Qualifications and Skills

• A minimum of 3 years and up to 7 years experience working with production-grade AI and machine learning systems.
• Strong proficiency in Python programming language and machine learning techniques.
• Hands-on experience with deep learning, natural language processing (NLP), computer vision, and generative AI models.
• Expertise in fine-tuning LLMs and SLMs for improved performance.
• Solid understanding of machine learning system architecture and deployment strategies.
• Experience in model serving and API development using FastAPI or Flask frameworks.
• Knowledge and practical experience with MLOps practices, including Docker, Kubernetes, and CI/CD pipelines.
• Familiarity with MLflow and Kubeflow for workflow orchestration and experiment tracking.
• Experience with cloud platforms such as AWS or Microsoft Azure for deploying AI and ML solutions.