Senior Full-Stack Engineer

Requirements

• Experience: Typically 5–8 years of professional full-stack software engineering experience.
• Frontend Stack: Deep expertise in React, TypeScript, and modern CSS/HTML. Ability to build complex, data-rich applications.
• Backend Stack: Proven experience with RDBs (e.g. PostgreSQL) and Object Stores (e.g. s3).
• API Design: Strong experience designing and consuming RESTful and/or GraphQL APIs with a focus on reliability and security.
• AI Development Tooling: Expert-level proficiency with modern agentic development environments (e.g., Cursor, Claude Code, Windsurf) to accelerate the shipping cycle.
• Architecture: Solid understanding of distributed systems, serverless architectures, and relational database modeling.
• Deployment: Familiarity with CI/CD practices and tools (e.g. Jenkins) and cloud providers for compute/network (e.g. AWS ALBs/ECS).
• Communication: Ability to bridge the gap between technical LLM capabilities and practical, user-facing product features.
• Agentic Frameworks: Hands-on experience with agentic orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, or PydanticAI).
• (Desirable) AI UX Patterns: Deep understanding of 'Agentic UX' patterns, such as ReAct, streaming UI updates, generative UI, and proactive notifications.
• (Desirable) LLM Observability: Experience with monitoring and evaluation tools for AI agents (e.g., LangSmith, Braintrust, or Helicone) to track agent reliability and cost.
• (Desirable) Tool-Calling Expertise: Proven track record of building 'Tools/Functions' for LLMs to interact with third-party SaaS platforms or internal databases.
• (Desirable) Durable Runtimes: Hands-on experience with durable runtime frameworks such as temporal or similar.
• (Desirable) Storage: Experience with Supabase e.g. Row Level Security (RLS), and database migrations.
• (Desirable) Vector Databases: Familiarity with pgvector or other vector stores for RAG (Retrieval-Augmented Generation) workflows.
• (Desirable) Real-time Systems: Experience with WebSockets or Supabase Realtime to provide instant feedback for long-running agentic tasks.

What the job involves

• As a Senior Full-Stack Engineer (3D), you will be a lead contributor in building the next generation of AI-native applications.
• You sit at the intersection of robust backend orchestration and intuitive frontend design.
• Your primary mission is to design and implement 'agentic' workflows/harnesses/systems where AI doesn't just respond to prompts but proactively executes tasks, manages state, and interacts with external tools to solve complex user problems.
• You will work across the entire stack, leveraging best in class frameworks and tooling for both frontend, data storage, runtime and API design.
• You will work closely with the UX Research team to understand and develop new interfaces and interaction techniques.
• End-to-End Engineering: Lead the development of full-stack features, from database schema design to high-fidelity UI implementation.
• Agentic Workflow Orchestration: Design and implement backend logic that allows AI agents to interact with APIs, databases, and long-running processes autonomously.
• Agentic UX Design: Build 'Human-in-the-loop' interfaces that allow users to monitor, steer, and collaborate with AI agents in real-time.
• Infrastructure & Security: Architect secure and performant backend systems using best practices and design patterns, to support high-concurrency AI operations.
• State Management: Manage complex, asynchronous application states where data may be updated concurrently by both users and background AI agents.
• Performance Optimization: Build efficient applications and services, understand how to best to maximize both perceived and actual user experience. Optimize the entire request-response lifecycle, focusing on minimizing latency in LLM streaming and database queries.
• Technical Health: Maintain high standards for code quality, focusing on static type safety, clean architectural consistency and comprehensive unit and integration testing.