Software Full Stack Engineer

About this role

About the role: - Build full-stack applications for a startup focused on AI agent systems and their integration into real-world workflows - Design and develop scalable backend services that power AI-driven features and user-facing products - Work across the entire stack, from API design through frontend implementation to cloud deployment - Architect systems from the ground up with emphasis on speed of iteration and technical excellence What you'll work on: - Creating RESTful and GraphQL APIs that serve both traditional and AI-enhanced applications - Selecting and implementing appropriate backend frameworks and patterns for different problem domains - Managing data persistence across multiple database types and choosing the right tool for each use case - Building event-driven systems with asynchronous processing to handle complex workflows - Deploying applications to cloud platforms and maintaining them in production environments - Integrating with AI agent frameworks and orchestration tools to embed intelligent behavior into applications What we're looking for: - Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, or a related field - 1-3 years of professional software development experience - Strong foundation in at least one backend technology stack from: Node.js/Express/NestJS, Python/FastAPI/Django, Go/Gin, Java/Spring Boot - Hands-on experience designing and shipping RESTful or GraphQL APIs - Solid grasp of relational and NoSQL databases including PostgreSQL, MongoDB, or Redis - Working ability with modern frontend frameworks such as React, Next.js, or Vue - Demonstrated experience with cloud deployment and operations on AWS, GCP, or equivalent platforms - Understanding of event-driven patterns, async job queues like Celery, RabbitMQ, or Kafka, and caching strategies - A builder's mindset: you think in terms of shipping working systems, not just writing code - Genuine curiosity about how artificial intelligence can solve real operational and business problems - Ownership of outcomes: you want to see your work reach users and create measurable value - Ability to communicate clearly and collaborate effectively in a dynamic team environment Bonus experience: - Familiarity with LLM orchestration frameworks such as LangGraph, AutoGen, LangChain, Semantic Kernel, or similar tools - Exposure to AI agent design, tool calling mechanisms, or prompt orchestration patterns - Knowledge of vector databases and embedding systems - Interest in MLOps practices, model fine-tuning, or evaluation frameworks