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.
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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.
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.
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.
