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.

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.

Artificial Intelligence (AI) & Machine Learning Engineer