The AI MLOps Engineer will focus on creating and testing innovative AI solutions aimed at improving software development productivity across the entire Software. This role will be responsible for rapid prototyping, developing MVPs, and iterating on product prototypes to integrate AI effectively into our software delivery process. The ideal candidate is experienced in both MLOps and AI experimentation, with a keen focus on delivering solutions that facilitate code generation, quality assurance, testing automation, and continuous delivery. Key Responsibilities AI Use Case Development and Prototyping Develop and rapidly iterate on AI-driven prototypes that support and streamline developer workflows, including code specification, code generation, and testing automation. Collaborate with product and DevSecOps teams to identify high-impact AI use cases that improve software development and delivery efficiency. Drive proof of concept (PoC) initiatives, transforming experimental AI ideas into feasible and scalable solutions Implement automation to improve repeatability and reduce manual tasks in the development pipeline, such as auto-code generation, static code analysis, and intelligent error detection. Integrate automation tools that improve developer productivity, streamline testing, and optimize release cycles. Stay current with the latest advancements in Al and machine learning technologies MVP Development and Iterative Testing Build Minimum Viable Products (MVPs) for new AI solutions, focusing on quick deployment, testing, and user feedback. Establish efficient testing and evaluation frameworks to assess the effectiveness of AI models and rapidly iterate on improvements. Collaborate with developers and QA teams to integrate AI-based prototypes into the broader software lifecycle and measure productivity impact. End-to-End ML Pipeline Development Design and deploy scalable ML pipelines tailored to rapidly evolving prototypes, with robust model training, testing, deploy