About the job

About The Role

Design, deploy, and maintain scalable AI and machine learning systems that deliver secure, reliable, and high-performing AI solutions. Ensure efficient model serving, deployment, monitoring, and operational excellence across AI environments.

Key Responsibilities

• Design, deploy, and maintain scalable AI/ML systems and infrastructure.
• Develop and manage MLOps pipelines for automated model deployment and monitoring.
• Ensure the performance, reliability, security, and scalability of AI platforms.
• Deploy, serve, and optimize machine learning and generative AI models for production environments.
• 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 to operationalize AI solutions.
• Monitor AI system performance, reliability, and availability, implementing continuous improvements.
• Troubleshoot production issues and optimize AI infrastructure.
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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 cloud platforms such as 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.
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Preferred Qualifications

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