At IBM, we're revolutionizing our approach to technology sales. Our Client Engineering teams are champions of co-creating solutions in real-time to solve complex business challenges. As an AI Engineer within our Client Engineering team, you'll harness your unique skills and perspectives to engage in the development and deployment of AI systems using our watsonx platform, creating 4-to-6-week pilots for clients, and contributing to IBM's story of growth and innovation. In this role, you'll partner with technical leaders across IBM and drive client engagements with a curiosity that sparks innovation and learning. Your contributions will form a cornerstone in our sales strategy, facilitating rapid client delivery and product innovation. At IBM the possibilities are endless. We offer extensive onboarding and ongoing development, fostering an environment where you can thrive and shape your own career trajectory. Surrounded by a supportive team, you'll be integral in creating user-centric, compelling pilots that lead clients to continually invest in IBM's people, products, and services. An AI Engineer at IBM is not just a job title - it's a mindset. You'll leverage the watsonx platform to co-create AI value with clients, focusing on technology patterns to enhance repeatability and delight clients. Success is our passion, and your accomplishments will reflect this, driving your career forward, propelling your team to success, and helping our clients to thrive. Your primary responsibilities will include: Proof of Concept (POC) Development: Develop POCs to validate and highlight the feasibility and effectiveness of the proposed AI solutions. Collaborate with development teams to implement and iterate on POCs, ensuring alignment with customer requirements and expectations. Collaboration and Project Management: Collaborate with cross-functional teams, including data scientists, software engineers, and project managers, to ensure smooth execution and successful delivery of AI solutions. Effectively communicate project progress, risks, and dependencies to stakeholders. Solution Implementation and Deployment: Oversee the implementation and deployment of AI solutions, working closely with development teams to ensure adherence to best practices, quality standards, and performance requirements. Provide technical guidance and support during the implementation phase. Solution Optimization and Performance: Continuously monitor and optimize the performance of AI solutions, including foundation models and large language models. Identify opportunities to enhance efficiency, accuracy, and speed through fine-tuning, algorithmic improvements, or infrastructure optimization. Customer Engagement and Support: Act as a technical point of contact for customers, addressing their questions, concerns, and feedback. Provide technical support during the solution deployment phase and offer guidance on AI-related best practices and use cases. Documentation and Knowledge Sharing: Document solution architectures, design decisions, implementation details, and lessons learned. Create technical documentation, white papers, and best practice guides. Contribute to internal knowledge sharing initiatives and mentor new team members. Industry Trends and Innovation: Stay up to date with the latest trends and advancements in AI, foundation models, and large language models. Evaluate emerging technologies, tools, and frameworks to assess their potential impact on solution design and implementation.