Join us as a Full‑Stack Engineer and help shape a greenfield system end‑to‑end with Java, React, and AWS - no legacy, no shortcuts. You’ll work hands‑on with AI‑augmented engineering, using LLMs and agentic workflows to drive architecture, automate workflows, and deliver production‑grade features.This role is offered on a freelance (B2B) contract.Requirements:Senior-level experience in software developmentStrong 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-offsPractical experience using AI tools (LLMs, coding assistants) in day-to-day workOwnership mindset and strong problem-solving skillsEnglish level: C1 AI Tooling Fluency &
- Augmented EngineeringWe'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or MCP integrations that accelerate the team's daily work.AI Judgment: Recognizing the failure modes (hallucinations, outdated knowledge, plausible-but-wrong code) and building habits that compensate for them.Nice to have:Experience in IoT projects cloudMain responsibilities:Designing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.
Spyrosoft