**Introduction** IBM Software infuses core business operations with intelligence—from machine learning to generative AI—to help make organizations more responsive, productive, and resilient. IBM Software helps clients put AI into action now to create real value with trust, speed, and confidence across digital labor, IT automation, application modernization, security, and sustainability. Critical to this is the ability to make use of all data, because AI is only as good as the data that fuels it. In most organizations data is spread across multiple clouds, on premises, in private datacenters, and at the edge. IBM’s AI and data platform scales and accelerates the impact of AI with trusted data, and provides leading capabilities to train, tune and deploy AI across business. IBM’s hybrid cloud platform is one of the most comprehensive and consistent approach to development, security, and operations across hybrid environments—a flexible foundation for leveraging data, wherever it resides, to extend AI deep into a business. **Your role and responsibilities** Your Role and Responsibilities* Are you passionate about building scalable backend systems and enabling cutting-edge AI solutions? Join IBM Watsonx Code Assistant for Z, where you’ll work at the intersection of generative AI and mainframe modernization. In this role, you’ll define best practices and strategies for leveraging AI across the product. You’ll work closely with cross-functional teams, including researchers, engineers, and product managers, to develop tools and frameworks for scalable AI-driven solutions. As an Senior AI Engineer, you will: * Analyze and refine prompts to enhance AI performance, focusing on clarity, context, and task alignment. * Collaborate with IBM Research to fine-tune AI models, ensuring they align with business objectives and user needs. * Analyze and align AI models with real-world scenarios and user needs to deliver contextually accurate and impactful solutions. * Define, design, and evolve agent-based solutions that allow users to interact seamlessly with AI systems. * Develop best practices and guidelines for effective prompt engineering, ensuring consistency across projects and teams. * Provide technical leadership and mentorship to engineering teams, guiding them on the effective use of prompts in AI applications. * Partner with product managers and stakeholders to identify high-impact use cases where prompt optimization can drive significant improvements. Why This Role is Unique: · Shape the Future of AI and Mainframes: You’ll be part of a team building the platform that powers AI-driven tools to modernize mainframe systems, a critical backbone for enterprises worldwide. · Impact at Scale: The work you do will impact some of the largest enterprise workloads globally, bridging mainframe reliability with next-gen innovation. · Grow with a Forward-Looking Team: Join a dynamic team working on strategic, industry-leading solutions with opportunities to grow and innovate alongside experts in AI and platform engineering. **Required technical and professional expertise** Required Professional and Technical Expertise* * Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field. * Proven experience working with large language models (LLMs) * Expertise in prompt engineering and natural language processing (NLP) techniques. * Proficiency in programming languages such as Python and experience with AI/ML libraries. * Strong knowledge of scenario alignment and strategies for adapting AI models to diverse use cases. * Strong knowledge of AI model evaluation techniques and prompt optimization strategies. * Hands-on experience with automation pipelines and tools for testing and deploying AI solutions. * Excellent problem-solving skills and the ability to work collaboratively in a cross-functional environment. **Preferred technical and professional experience** Preferred Professional and Technical Expertise * Experience with cloud platforms such as AWS, Azure, or IBM Cloud for deploying AI systems. * Familiarity with containerization technologies like Docker and Kubernetes. * Knowledge of CI/CD pipelines for AI/ML models. * Experience in developing tools for automating prompt testing and evaluation. * Understanding of user experience (UX) principles in AI applications. * Familiarity with Agile development methodologies.