DepartmentDigital Engineering, Digital Engineering : Digital Engineering
Company size5,001–10,000 people
PostedOct 9, 2026 · 3d ago
Verified live6h ago
At a glanceSummarised by Seekless from the posting.
Must have16
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
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
Comfortable using AI tools in day-to-day engineering work
Familiarity with Retrieval Augmented Generation (RAG) architectures
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
Skills
Java
Python
Node.js
NestJS
React
GCP
Azure
SQL
NoSQL
Docker
Kubernetes
REST API
GraphQL
LangChain
LangGraph
Google GenAI
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.