• Architect and implement new cloud infrastructure to enable high-performance SaaS applications globally in the cloud. • Drive infrastructure features end-to-end, from design documentation through implementation, rollout, and operational ownership. • Build and deliver observability tools and analyze data, working with the application development team to ensure a consistently superb customer experience. • Continue to grow automation for infrastructure provisioning, developer efficiency, and internal tooling efficiency. • Apply AI-assisted development tools to accelerate IaC, automation, code review, and on-call workflows, while maintaining a high bar for security, correctness, and review discipline. • Mentor fellow engineers and contribute to team-wide architecture decisions. • Assist our customer support and sales teams with technical issues regarding the Optro application. • Maintain reliability for our production systems to exceed our SLA requirements, including participation in an on-call rotation for production issues. • Help define internal Service Level Objectives and Service Level Indicators for the Infrastructure Engineering team to drive better reliability. • Collaborate with engineers, designers, and product managers; give and receive feedback well in a dynamic environment.

• Own full-stack feature delivery from schema design and API implementation through React UI and deployment • Design and build scalable RESTful and GraphQL APIs, with a focus on clarity, performance, and developer ergonomics • Architect and integrate Azure-hosted services including Azure Functions, Cosmos DB, Azure AI, and Azure DevOps pipelines • Embed AI capabilities into products wrapping LLM APIs, building prompt pipelines, and integrating Azure OpenAI or Cognitive Services into features • Use AI tools actively in your daily workflow: generation, debugging, test coverage, and documentation not as a crutch but as a force multiplier • Participate in code reviews, architecture discussions, and technical planning with engineering teams and client stakeholders • Instrument, monitor, and optimize performance across the stack from slow queries to front-end bundle bloat • Contribute to raising the engineering bar: documentation, tooling, shared patterns, and mentoring less senior teammates

• Own full-stack feature delivery from schema design and API implementation through React UI and deployment • Design and build scalable RESTful and GraphQL APIs, with a focus on clarity, performance, and developer ergonomics • Architect and integrate Azure-hosted services including Azure Functions, Cosmos DB, Azure AI, and Azure DevOps pipelines • Embed AI capabilities into products wrapping LLM APIs, building prompt pipelines, and integrating Azure OpenAI or Cognitive Services into features • Use AI tools actively in your daily workflow: generation, debugging, test coverage, and documentation not as a crutch but as a force multiplier • Participate in code reviews, architecture discussions, and technical planning with engineering teams and client stakeholders • Instrument, monitor, and optimize performance across the stack from slow queries to front-end bundle bloat • Contribute to raising the engineering bar: documentation, tooling, shared patterns, and mentoring less senior teammates

• Build and maintain robust ML systems using Python (Pandas/NumPy, PyTorch/transformers) to support efficient ML operations. • Design ML cloud infrastructure using AWS services, ensuring the platform is built for reuse, scalability, and high-volume throughput. • Collaborate with application engineers to integrate, test, and monitor ML model services across SurveyMonkey’s microservices architecture. • Act as a consultant to internal feature teams, educating and influencing decisions regarding ML tooling and infrastructure. • Incorporate telemetry into the platform for complex failure mode analysis, ensuring real-time reliability for millions of daily survey responses. • Serve as a technical leader and mentor, championing high standards in coding, documentation, and architectural best practices to foster the professional growth of junior engineers on the MLP team.