Fullstack Software Engineer (Java, React, AI)
Please mention DailyRemote when applying
AI Summary
Requirements:
5+ years of experience in software development
Strong experience with Java (Spring Boot) and React (TypeScript)
Hands-on cloud experience (AWS)
Experience building and running production systems (REST APIs, relational databases, CI/CD, containers)
Solid understanding of architecture, design patterns, and trade-offs
Practical experience using AI tools (LLMs, coding assistants) in day-to-day work
Ownership mindset and strong problem-solving skills
English level: C1 AI Tooling Fluency & Augmented Engineering
We're looking for an engineer who has genuinely internalised AI into their engineering practice. Your expertise should include:
Agentic Workflows & Tool Chaining: Ability to design and run multi-step AI agent workflows - from technical research, through code generation, to validation against architectural constraints.
AI-Driven Architectural Research: Proficiency in using LLMs to compare frameworks, evaluate libraries, analyze RFCs and standards, and produce well-grounded architectural recommendations.
AI-Driven Code Generation & Refactoring: Hands-on experience generating Java services, React components, integration tests, and migration scripts using modern AI assistants.
Workflow & Tooling Automation: Building small custom tools, scripts, or MCP integrations that accelerate the team's daily work.
AI Judgment: Recognizing the failure modes (hallucinations, outdated knowledge, plausible-but-wrong code) and building habits that compensate for them.
Nice to have:
Experience in IoT projects cloud
Main responsibilities:
Designing and delivering production-grade features end-to-end across Java (Spring Boot) backend and React frontend.
Architectural Decisions: Driving full-stack architectural choices on a greenfield codebase - data models, API contracts, service boundaries, frontend state architecture. No legacy to hide behind.
AI-Augmented Engineering: Using AI agents and LLM-based tooling daily - for technical research, evaluating libraries and design trade-offs, generating and refactoring code, debugging, and writing tests.
Workflow Automation: Identifying repetitive parts of the development lifecycle (code review prep, documentation, ticket grooming, test scaffolding, release notes) and building AI-driven automations around them.
Research & Decision Support: Leveraging LLMs and agentic workflows to rapidly digest technical documentation, RFCs, and vendor specs - turning hours of reading into actionable architectural input.
Business Partnership: Working directly with business stakeholders - translating fuzzy requirements into clear technical scope, pushing back where it matters, and co-shaping the roadmap.
Scrum & Team Dynamics: Operating in a Scrum cadence with the maturity to keep ceremonies useful and lightweight, not bureaucratic.
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