Software Development Engineer - R01571170

Job description

**Software Development Engineer** ### Job requirements - Proven experience building and shipping full-stack applications in a professional product-based environment. - Ability to independently own and deliver features or systems across sprint/release cycles. **Mandatory Skills & Responsibilities** - Strong hands-on experience with at least one modern backend stack, such as Java, Python, or Node.js/NestJS, with solid understanding of performance tuning, memory management, and concurrency. - Strong experience with modern frontend development using React and SSR-based frameworks where applicable, including routing, state management, and performance optimization. - Working knowledge of a major cloud platform such as GCP or Azure, including production deployments, containers, serverless services, messaging, IAM, and cost-aware operations. - Strong SQL fundamentals, including query optimization, indexing, schema design, and data modeling, with working knowledge of at least one NoSQL database. - Proven ability to design scalable and resilient systems using caching, load balancing, and event-driven architecture. - Comfortable working in an Agile/Scrum environment, with a track record of independently delivering features across sprints and releases. - Strong Git practices, including sensible branching strategies and clean release management. - Experience designing REST and/or GraphQL APIs, including versioning and backward compatibility. - Strong testing mindset, with experience in unit, integration, and contract testing. - Hands-on experience with Docker and container orchestration platforms such as Kubernetes. **AI Skills:** - Comfortable using AI tools in day-to-day engineering work to move faster, work smarter, and stay cost-aware while doing it - Familiarity with Retrieval Augmented Generation (RAG) architectures — semantic layers for data retrieval and grounding LLM responses in real, trustworthy data - Practical experience with at least one LLM application framework (LangChain, LangGraph, Google GenAI tools, or similar) for prototyping and integrating AI-driven features - Exposure to agentic or multi-agent AI systems — agent roles, tool use, memory, and workflow orchestration.