• Drive the technical architecture across the domain, with a focus on modernization, scalability and AI integration. • Lead the design and implementation of microservices and cloud-native systems. • Guide the transition from legacy systems to modern distributed systems. • Collaborate with senior stakeholders (EMs, Staff and Principal Engineers, Directors) to align on technology direction. • Champion engineering excellence, fostering a culture of autonomy, accountability, and quality. • Provide mentorship and leadership across engineering teams. • Integrate LLMs and other GenAI models into web applications through efficient API design and implementation. • Build and optimize API endpoints enabling seamless, real-time communication between front-end applications and back-end AI services. • Design and develop secure, scalable, and high-performing Java-based microservices for AI model deployment. • Develop robust back-end systems in Java to support deployment, scalability, and ongoing maintenance of GenAI models. • Build and maintain data pipelines, including preprocessing input data and post-processing model outputs for application use. • Implement best practices for sensitive data handling and maintaining high model performance. • Use Kubernetes and Docker for containerization and orchestration to ensure scalable deployment of AI applications. • Implement CI/CD pipelines for automated testing and delivery of code changes. • Maintain scalable and secure cloud infrastructure using platforms such as Google Cloud Platform or Azure for model training, storage, and deployment. • Utilize vector databases (e.g., Pinecone, Weaviate, Faiss) for embedding management and similarity search. • Work with frameworks supporting model development and deployment, including Hugging Face, LangChain, and OpenAI ecosystem tools. • Optimize and fine-tune LLMs based on specific application needs.

• Process massive streams of media metadata in a high-scale AWS environment. • Deliver high-quality features that power industry-leading measurement and attribution products. • Independently own and deliver features, writing high-quality code that includes monitoring, metrics, and logging as standard practice. • Author and own the testing plan for features, performing various test types and identifying patterns in test results. • Actively participate in code reviews to ensure a healthy and maintainable codebase. • Use AI tools to improve task speed and quality, critically evaluate AI-generated output, and identify enhancements in work assignments. • Participate in the on-call rotation and contribute to defining on-call expectations.

• Build Innovative AI-Driven Applications: Design, develop, and deploy AI-powered applications that solve complex business problems, integrating Dataiku’s backend AI capabilities with user-centric, intuitive front-end interfaces. • Work with Data Scientists and Industry Specialists: Collaborate with solutions engineers, data scientists, and industry experts to develop the best solutions for customers. • Empower Business Users: Create applications that allow expert users to work with large datasets and extract meaningful insights quickly and intuitively. • Drive Continuous Improvement: Take responsibility for the end-to-end lifecycle of applications—from PoC to production. • Collaborate Across Teams: Work closely with core teams to ensure high-quality, reusable components. • Contribute to Team Growth: Help mentor new team members, participate in interviews, and contribute to onboarding processes.

• Design, develop, and deploy AI-powered applications • Collaborate with solutions engineers, data scientists, and industry experts • Create applications that allow expert users to work with large datasets • Take responsibility for the end-to-end lifecycle of applications • Ensure high-quality, reusable components • Help mentor new team members