Software Development Engineer - R01571170.

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.