AI/MLOps Engineer

We are seeking an AI/MLOps Engineer to design, build, and operationalize machine learning solutions within an Azure-based ecosystem. This role focuses on productionizing data science models, building scalable ML pipelines, and enabling advanced analytics through robust data and AI platforms. You will partner closely with data scientists, data engineers, and DevOps teams to move models from experimentation into reliable, secure, and scalable production environments. Key Responsibilities Solution Design & Deployment: Design, develop, and deploy AI/ML solutions using Azure services (e.g., Azure Machine Learning, Cognitive Services). Pipeline Engineering: Build and maintain end-to-end machine learning pipelines for training, testing, deployment, and monitoring. Model Productionization: Transition models developed by data scientists into production, ensuring high scalability, performance, and reliability. CI/CD Implementation: Implement CI/CD pipelines to support continuous integration and delivery of data, code, and models. Lifecycle Automation: Automate model deployment, monitoring, and lifecycle management processes. Performance Monitoring: Continuously monitor model performance and manage retraining/updates. Systems Integration: Integrate AI capabilities into enterprise data workflows and applications. Advanced AI Development: Develop solutions leveraging NLP, computer vision, recommendation systems, and chatbot technologies. Cross-Functional Collaboration: Partner effectively with Data Engineering, Data Science, and DevOps teams. Governance & Security: Ensure the security, governance, and compliance of ML systems and data pipelines. Required Qualifications Education: Bachelor’s degree in Computer Science, Information Systems, or a related field (or equivalent practical experience). Experience: 3–6 years of experience in software/data engineering or machine learning, with recent hands-on MLOps experience. Programming: Strong proficiency in Python (preferred), Java, or Scala. ML Frameworks: Proficiency with frameworks such as TensorFlow, PyTorch, or scikit-learn. Cloud Platforms: Extensive hands-on experience with Azure cloud services, specifically Azure Machine Learning. Containerization: Experience with containerization and orchestration tools (Docker, Kubernetes). MLOps Tooling: Familiarity with MLOps frameworks such as MLflow, Kubeflow, or similar technologies. Production Experience: Proven success in building and deploying ML models into live production environments. Preferred Experience Specialized AI: Experience implementing NLP, computer vision, or recommendation systems. Conversational AI: Exposure to chatbot or conversational AI frameworks. Regulated Environments: Experience working within regulated industries (e.g., healthcare, finance). Data Architecture: Familiarity with enterprise data platforms and modern, scalable data architectures. Show more Show less