• Develop and maintain software applications using Python and .NET. • Collaborate with cross-functional teams to define, design, and implement new features. • Ensure code quality through unit testing and code reviews. • Participate in agile ceremonies and contribute to continuous process improvement. • Diagnose and resolve software defects and issues.

• Execute database migrations with enterprise and Private Cloud customers. • Assume ownership of the PlanetScale relationship and migration work. • Productise repeatable migration patterns. • Automate recurring migration requests. • Own broader app and service migrations in the gaps. • Distribute what you learn. • Keep the database offering competitive.

• Develop and maintain scalable frontend applications using React 19 and TypeScript. • Build and optimize applications within a Turbo monorepo using pnpm workspaces. • Implement routing with TanStack Router and leverage Module Federation in a micro-frontend architecture. • Develop backend utilities and platform services with Fastify. • Create responsive, reusable UI components using Tailwind CSS, the shared Design System, and Storybook. • Design and maintain type-safe APIs and data contracts with Zod. • Ensure software quality through unit, integration, end-to-end, accessibility, and visual regression testing using Vitest and Playwright. • Maintain high code quality using Biome for linting and formatting. • Monitor application performance, logging, and observability with OpenTelemetry and Prometheus.

• Build, refine, and use ML Engineering platforms and components; develop and implement scalable backend systems, APIs, and microservices using FastAPI. • Implement MLOps including model KPI measurement, tracking, model drift detection, and model feedback loops. • Deploy and operationalize ML and Deep Learning models, with a strong focus on LLMs and Generative AI. • Integrate Azure OpenAI (GPT-4, GPT-4 Vision) and other LLM providers with proper retry logic and error handling. • Maintain up-to-date knowledge of state-of-the-art technologies such as LLMs, GenAI, and transformer architectures. • Scale machine learning algorithms to work on massive data sets under strict SLAs. • Build and orchestrate model pipelines including feature engineering, inferencing, and continuous model training. • Write backend application code in Python and SQL using strong object-oriented principles and asynchronous programming (asyncio, async/await). • Implement dependency injection patterns and layered architecture (Service, Foundation, Orchestration, DAL). • Build LLM observability (e.g., Langfuse) to track prompts, tokens, costs, and latency. • Develop prompt management systems with versioning and fallback mechanisms. • Implement Celery (or similar) workflows for asynchronous task processing and complex pipelines. • Build multi-tenant architectures with client data isolation. • Implement cost optimization strategies for LLM usage (prompt caching, batch processing, token optimization). • Integrate third-party APIs and services (e.g., document/OCR services, cloud storage, enterprise systems). • Collaborate with client-facing teams to understand business context and contribute to technical requirement gathering. • Write production-ready code that is testable, maintainable, and accounts for edge cases and errors. • Ensure high quality of deliverables by following architecture/design guidelines, coding best practices, and periodic design/code reviews. • Write unit tests and higher-level tests to handle expected edge cases and errors gracefully. • Troubleshoot backend application code using structured logging and distributed tracing. • Use bug tracking, code review, version control, and other tools to organize and deliver work. • Participate in scrum calls and agile ceremonies, communicating progress, issues, and dependencies. • Document application changes and updates, including API documentation via OpenAPI/Swagger. • Research and evaluate emerging architecture patterns and technologies through rapid learning, proofs-of-concept, and prototypes.