AI/MLOps Engineer

THIS ROLE IS NOT OPEN TO C2C, VISA SPONSORSHIP, or THIRD PARTIES

We are seeking a highly skilled and passionate AI/MLOps Engineer to design, build, and operationalize scalable machine learning solutions on Azure. This role focuses on creating robust data platforms and pipelines that enable advanced analytics and production-grade AI applications. The ideal candidate bridges the gap between data science and engineering by transforming experimental models into reliable, efficient, and scalable systems.

AI/MLOps Engineers are responsible for automating and optimizing the lifecycle of machine learning models, ensuring seamless integration into business workflows and delivering real-world impact through intelligent systems.

Key Responsibilities

• Design, develop, and deploy AI/ML applications using Azure Machine Learning, Azure Cognitive Services, and Azure Bot Services.

• Build and maintain scalable data pipelines and ML workflows to support model training, validation, and deployment.

• Collaborate with data scientists and data engineers to integrate AI components into production data environments.

• Develop and implement machine learning models, including NLP solutions, recommendation systems, chatbots, and image recognition applications.

• Automate CI/CD pipelines for machine learning systems, incorporating data, code, and model versioning.

• Deploy, monitor, and scale machine learning models in production environments.

• Continuously evaluate model performance and implement improvements to maintain accuracy and efficiency.

• Ensure security, governance, and compliance of AI/ML systems.

• Partner with DevOps teams to streamline infrastructure and deployment processes.

Required Qualifications

• Bachelor’s degree in Information Systems, Computer Science, or a related field, or equivalent practical experience.

• 3–6 years of experience delivering end-to-end machine learning solutions, including at least 18 months focused on MLOps.

• Strong programming skills in Python, Java, or Scala.

• Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.

• Proficiency with containerization and orchestration tools, including Docker and Kubernetes.

• Experience with ML lifecycle and deployment tools such as MLflow or Kubeflow.

• Solid experience working within the Azure cloud ecosystem.

• Understanding of CI/CD practices for machine learning systems and data pipelines.

Preferred Skills

• Experience with natural language processing (NLP) and conversational AI solutions.

• Familiarity with data engineering concepts and distributed data processing.

• Knowledge of monitoring tools and model observability best practices.

Additional Information

• Ability to travel up to 25% as needed.