• Own the technical pre-sales motion for assigned enterprise opportunities • Lead discovery workshops and deliver compelling product demonstrations • Design and deliver tailored solutions based on customer needs • Support customer proof-of-concepts (POCs) and pilots while managing timelines • Partner with Professional Services to shape implementation approach and scope

• Design and implement SAP Analytics Cloud • Connect to various data sources • Code the cloud application designer to develop logic in SAC • Configure data actions and multi actions • Work on positioning and enhancements

• Collaborate with GTM, Product, and Engineering to define and deliver technical solutions aligned with business goals. • Serve as the primary technical point of contact for customers — from onboarding to production deployment. • Design, test, and implement integrations leveraging tools like Elasticsearch, Snowflake, AWS S3, AWS Lambda, and PostgreSQL. • Build and maintain API and data workflows using REST, webhooks, and custom scripts in Python. • Act as a trusted advisor, providing prescriptive guidance across risk domains such as Fraud, AML/KYC/KYB, and Credit Underwriting. • Translate customer feedback into actionable insights for the Product and Engineering teams.

• Design and oversee the development of scalable generative AI systems and enterprise-grade AI platforms. • Establish robust architectures that support model training, inference, monitoring, and lifecycle management in production environments. • Direct the selection, customization, and enhancement of state-of-the-art generative AI and large language models. • Develop and execute APIs, microservices, and integration frameworks to incorporate AI capabilities into enterprise applications. • Ensure that AI platforms meet stringent standards for performance, reliability, security, and scalability, while also adhering to data governance and privacy regulations. • Collaborate closely with product, engineering, and business teams to outline technical requirements and approaches to AI architecture. • Architect end-to-end pipelines for deploying and monitoring AI models, ensuring seamless integration with existing systems. • Guide architectural decisions for LLM applications, AI workflows, and distributed AI infrastructure. • Institute best practices for ethical AI development, including strategies to mitigate risks like model hallucinations, bias, and reliability challenges. • Provide technical mentorship and guidance to engineering teams, while contributing to the formulation of long-term technology strategies and the advancement of AI platforms.