Senior AI Platform & MLOps Engineer

RDC Talent supports organizations in navigating talent and organizational transformation in the age of AI, offering end-to-end solutions across AI Transformation Consulting, AI & IT Recruitment, Outsourcing, Strategic HR Consulting, and Leadership & Competency Development.With 20+ years of experience, a team of industry experts, and work delivered for 500+ companies, we help organizations across technology, fintech, banking, e-commerce, insurance, and other industries address their evolving needs by connecting them with the right talent and organizational structures.If you think you have most of the following Qualifications;Bachelor's degree in Computer Engineering, Software Engineering, Electrical and Electronics Engineering, or a related engineering discipline.Minimum 5 years of experience in Software Development, DevOps, Platform Engineering, or MLOps.Hands-on experience with Linux, Docker, and Kubernetes technologies, as well as knowledge of the installation, configuration, and management of Kubernetes-based platforms.Experience in Python and REST API development, with knowledge of secure API design, authentication, and authorization mechanisms.Experience with CI/CD processes and automation tools.Knowledge of Machine Learning model deployment, serving, versioning, and lifecycle management processes.Knowledge of MLOps processes.Knowledge of system architecture, scalability, high availability, and performance.Ability to prepare technical documentation and collaborate effectively with different IT teams.Participate in the design and development of the architecture of the in-house artificial intelligence platform.Provide technical support in determining the technical requirements of GPU-based AI infrastructure and hardware procurement processes.Design and configure the infrastructure required to run AI/ML workloads on Red Hat OpenShift AI.Establish, standardize, and automate Machine Learning Operations (MLOps) processes.Ensure the design and implementation of the Model Registry infrastructure and manage its technical lifecycle.Develop and manage the deployment, serving, versioning, and monitoring processes of ML and LLM models.Develop service and access architectures that enable AI models to be securely and consistently consumed by internal applications.Participate in the development of AI Gateway, API management, and model access layer components.Adapt CI/CD and automation processes to AI/ML workloads.Monitor the performance, scalability, resource utilization, and sustainability of AI services.Establish shared platform components and technical standards for AI development teams.Prepare documentation for developed infrastructure components and provide technical knowledge transfer to relevant IT teams.