• Partner with Product and stakeholders to understand workflows, needs, and problem spaces • Translate ambiguous requirements into clear technical plans and system designs • Validate solutions through real usage and feedback loops, not just specs • Communicate progress, risks, and trade-offs clearly to technical and non-technical audiences • Ensure engineering work is always anchored to customer value and business outcomes • Design and evolve system architecture for scalable, reliable platforms • Contribute directly to core systems across the full stack: frontend, backend, and infrastructure • Own key technical decisions and guide architectural direction • Drive best practices across CI/CD, testing, observability, and release processes • Deliver outcomes end-to-end, from design through production • Build and integrate AI-powered capabilities into production systems • Tune model behavior, evaluate outputs, and iterate toward reliable results • Design and optimize inference pipelines with attention to latency, cost, and reliability • Apply LLM capabilities (tool-use, prompt design, evaluation) to solve real problems, not research • Lead and develop engineers through coaching, feedback, and growth support • Practice servant leadership: remove blockers, enable success, set the standard through your own work • Build a culture of ownership, accountability, and continuous improvement

• Design, develop, test, and deploy scalable web applications and APIs across the full stack — from database schema to UI components • Create AI applications where appropriate to enhance the business • Lead architectural decisions and code reviews, ensuring high standards for maintainability, security, and performance • Participate actively in R&D efforts: evaluate new technologies, build proof-of-concept prototypes, and contribute findings to engineering roadmap discussions • Collaborate with product and design teams during sprint planning and discovery sessions to shape technical solutions • Optimize application performance, identify bottlenecks, and implement improvements across front-end and back-end systems • Write comprehensive unit, integration, and end-to-end tests to ensure software reliability • Contribute to internal technical documentation, architecture diagrams, and R&D reports • Mentor junior developers through pair programming, code reviews, and knowledge-sharing sessions • Stay current with industry trends and proactively recommend tooling or process improvements • Participate in on-call rotation and support incident response as needed

• Build and maintain backend services and APIs in Python, using AI-assisted development and automation as a standard part of the workflow • Own features end-to-end, from design through production, with accountability for quality, performance, and maintainability • Actively drive AI adoption within the engineering team by introducing, refining, and scaling AI-driven workflows across the development lifecycle • Identify opportunities where AI can reduce manual effort, improve code quality, accelerate delivery, or improve operational reliability • Develop and support single-page applications using Vue • Work closely with engineers in small, cross-functional feature teams • Collaborate with Product Managers and other teams to deliver measurable business value • Partner with your Engineering Manager to support technical growth, mentorship, and career development • Support other tasks or projects as assigned to meet team and business needs

• Design and evolve hyper-scale systems capable of handling millions of transactions per second using advanced sharding, event-driven architectures, and high-performance caching strategies • Lead architectural decision-making through RFC processes, ensuring scalability, reliability, and long-term maintainability • Collaborate cross-functionally with engineering, SRE, and DevOps teams to align infrastructure with application needs • Contribute hands-on by building proofs of concept (PoCs) for new technologies such as messaging systems or storage solutions • Define system architecture for a microservices-based, cloud-native SaaS platform integrating APIs, relational and non-relational databases, and AI-driven workflows • Mentor and guide senior engineers, fostering strong systems thinking, design excellence, and technical leadership across teams • Drive innovation in AI-enabled architectures and support the evolution of intelligent, scalable platform capabilities • Support other tasks or projects as assigned to meet team and business needs