• Design, develop, test, and deploy software solutions that address business requirements, ensuring scalability, performance, and security with an AI-first lens • Write clean, efficient, and maintainable code using modern programming languages and frameworks, such as C#, .NET, Java, Python, or JavaScript • Collaborate with cross-functional teams, including product management, QA, and DevOps, to define technical requirements, deliver features, and resolve issues • Build and maintain APIs, microservices, and cloud-based applications on platforms such as Azure, AWS, or Google Cloud • Participate in all phases of the software development lifecycle (SDLC), including design, development, testing, deployment, and maintenance • Utilize modern development practices, such as Agile methodologies, CI/CD pipelines, version control (e.g., Git), and automated testing • Troubleshoot and resolve software bugs, performance bottlenecks, and technical issues to ensure a seamless user experience • Continuously improve existing systems by identifying opportunities for optimization, refactoring, and modernization • Collaborate with peers to conduct code reviews and ensure adherence to coding standards, security guidelines, and best practices • Stay current with emerging technologies, tools, and frameworks, recommending their adoption when appropriate to enhance the development process.

• Lead growth engineering projects: implementing localization frameworks, building referral program logic, and tracking improvements • Build and ship features on Buffer's marketing site: landing pages, interactive tools, and conversion flows • Shape marketing platform capabilities by creating systems for A/B testing and page generation • Drive experimentation and optimization: implement A/B tests, optimize funnels, and iterate based on learnings • Maintain marketing technology stack integrations and ongoing system health • Contribute to engineering culture by partnering with engineers and sharing knowledge about growth systems

• Design and code RAG systems with embeddings, hybrid search, and evaluation pipelines. • Develop hands-on multi-agent orchestration frameworks (LangGraph, AutoGen, CrewAI, or custom). • Implement and maintain LLMOps pipelines for prompt versioning, cost tracking, and evaluation. • Integrate AI workflows with backend services and data layers for real-world scalability. • Experiment with LLMs for retrieval, summarization, and personalization use cases. • Contribute directly to code, architecture reviews, and performance improvements. • Collaborate with data and platform engineers to deploy and optimize GenAI solutions.

• Conducir el desarrollo de soluciones de software innovadoras y eficientes. • Contribuir al diseño y evolución de la arquitectura del sistema. • Participar activamente en actividades de codificación y desarrollo, manteniendo altos estándares de calidad. • Realizar revisiones de código y asegurar la calidad del software.