• Supports our product, sales, and consulting teams with building and maintaining scalable, web-based applications. • Design, develop, and maintain full-stack web applications that support Generative AI projects. • Work closely with AI researchers to integrate Generative AI models into applications, ensuring seamless functionality and optimal performance. • Monitor and optimize the performance of applications and AI integrations to ensure responsiveness and efficiency. • Coordinate with data scientists, engineers, AI researchers, and other developers to understand project requirements and deliver solutions that meet technical and business needs. • Identify and resolve issues related to application performance, AI model interactions, and data handling. • Apply best practices in coding, testing, and deployment to ensure high-quality software development and robust application performance. • Keep abreast of the latest developments in AI technologies, and software engineering practices.

• Design & Build Across the Stack – Design and implement features end-to-end, from robust backend services and APIs in Java/Kotlin and Spring Boot to polished, responsive user interfaces in Angular. Own the full lifecycle of features - database schema to UI component - while maintaining high code quality standards and contributing to architectural decisions. • Collaborate & Deliver – Work within agile product teams to deliver high-quality features, participate in code reviews, and contribute to technical discussions across both backend and frontend. Partner with designers and product managers to translate requirements into complete, working solutions. • Innovate & Improve – Tackle complex technical problems that span the full stack, propose innovative solutions and continuously improve our development practices, tooling, and user experience. • AI-Accelerated Development – Leverage AI coding assistants and tools to accelerate development velocity across the full stack. Help establish and refine AI-assisted development workflows, prompting strategies, and best practices for the team. Evaluate and adopt emerging AI tools that improve code quality, testing, and developer productivity. Contribute to a culture where AI is a natural part of the engineering workflow, not an afterthought.

• Design, build, and operate backend services in Python that power AI-driven products and shared capabilities. • Build and maintain resilient service integrations across internal and external systems, handling failure modes, rate limits, and interface changes. • Own ambiguous, end-to-end problems: from early design and architecture through implementation, rollout, and iteration in production. • Develop and evolve LLM-backed features and agentic workflows used in production, with a focus on reliability, observability, and real-world behavior. • Contribute to core AI platforms and enablement systems—services that your team uses directly and that other engineers can build on. • Collaborate with product managers, researchers, and other engineers across teams to turn loosely defined AI use cases into concrete, production-ready systems. • Help shape evaluation and quality strategies for AI-powered features, including building or extending evaluation harnesses, benchmarks, or feedback loops. • Act as a technical leader for the work you own—making sound design decisions, coordinating with stakeholders, and contributing to design and code reviews.

• Design and implement tools, frameworks, and processes that enhance developer productivity and streamline the software development lifecycle • Integrate AI in all of our existing processes to make us faster and more agile • Architect solutions for breaking down monolithic systems into modular, maintainable services • Champion and implement AI-powered tools as productivity multipliers across engineering workflows • Define metrics and track improvements in engineering efficiency, cycle time, and developer experience • Build consensus around engineering standards and best practices that can be adopted company-wide • Collaborate with engineering leaders to identify bottlenecks in development workflows and implement solutions • Proactively identify opportunities for improvement in developer experience without waiting for direction • Evangelize engineering experience improvements and share success metrics with the broader organization • Mentor other engineers on software architecture, system design, and development best practices