• 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.

• Design, build, and operate scalable backend services supporting Fintech, Checkout, Promotions, Loyalty, and Payment systems. • Develop clean, maintainable, and high-performance production code with readability and long-term ownership in mind. • Design and evolve RESTful APIs and integrations with internal platforms, payment gateways, and third-party services. • Build reliable distributed systems capable of handling high traffic, failures, and asynchronous workloads. • Drive architectural decisions with scalability, resilience, maintainability, and operational excellence in mind. • Optimize application performance across services, databases, caching layers, queues, and background workers. • Participate in architecture discussions, technical design reviews, RFCs, and engineering planning. • Ensure services are production-ready through monitoring, logging, alerting, testing, and observability best practices. • Write comprehensive unit and integration tests while maintaining high engineering standards through code reviews. • Leverage AI-assisted engineering tools responsibly to accelerate development, debugging, testing, documentation, and code quality. • Mentor junior and mid-level engineers through technical leadership, design discussions, and engineering best practices. • Contribute throughout the full software development lifecycle, from design through deployment and production support. • Continuously improve system reliability, developer experience, and engineering standards across the team.

• Lead and mentor engineering teams in the development of AI-driven solutions. • Utilize deep practical experience with various AI models and frameworks to inform design and implementation strategies. • Design scalable, performant, and secure architectures for AI systems. • Manage the full lifecycle of AI projects, including conceptualization, deployment, and maintenance. • Identify and resolve complex technical challenges associated with AI implementation. • Articulate technical vision and provide clear guidance, fostering collaboration within the team and with stakeholders. • Remain involved in coding, model development, and the review of technical designs.