AI​/MLOps Engineer

THIS ROLE IS NOT OPEN TO C2C, VISA SPONSORSHIP, or THIRD PARTIES. CONTRACT TO HIRE (EST or CST).

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.

• 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 Dev Ops teams to streamline infrastructure and deployment processes.

• 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 Tensor Flow, 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.

• 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.

• Ability to travel up to 25% as needed.