Artificial Intelligence (AI) & Machine Learning Engineer

Role overview

Key responsibilities

• Design, deploy, and maintain scalable AI/ML systems and infrastructure.
• Develop and manage MLOps pipelines for automated model deployment and monitoring.
• Ensure AI platform performance, reliability, security, and scalability.
• Deploy, serve, and optimize machine learning and generative AI models in production.
• Build and maintain CI/CD pipelines for AI applications.
• Manage containerized AI applications using Docker and Kubernetes.
• Collaborate with data scientists, software engineers, and business stakeholders.
• Monitor AI system performance, reliability, and availability and implement improvements.
• Troubleshoot production issues and optimize AI infrastructure.

Requirements

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.
• 3–8 years of experience in AI systems engineering, MLOps, or machine learning platform engineering.
• Strong programming skills in Python.
• Experience with Microsoft Azure, AWS, or Google Cloud Platform.
• Hands-on experience with Docker, Kubernetes, and containerized deployments.
• Experience designing and maintaining CI/CD pipelines.
• Knowledge of distributed systems and scalable AI infrastructure.
• Experience deploying and operationalizing machine learning and generative AI solutions.

Preferred qualifications

• Experience with Azure Machine Learning, AWS SageMaker, or Google Vertex AI.
• Experience with Infrastructure as Code, such as Terraform or similar tools.
• Familiarity with AI monitoring, observability, and model lifecycle management.
• Relevant cloud or AI certifications.