Senior Full-Stack Developer (AI-Native)

We are looking for a Senior Full-Stack Developer (AI-Native) to join the project full-time. This person will own the full stack — backend, frontend, and AI/agent layer — with no splitting between projects. They need to be a thinking partner, not an executor — someone the client's technical leadership wants to work with directly.

Your responsibilities will include:

• Own the full stack end-to-end — backend, frontend, and AI/agent layer — architecture decisions, performance work, integration, testing — without hand-holding.
• Work directly with the client's technical leadership as a thinking partner.
• Build and maintain LLM-powered agent systems with observability, evaluation, and structured tracing.
• Cover backend, frontend, and infrastructure independently — no "that's not my area."
• Use AI-assisted development (Claude Code or equivalent) fluently and extensively as a primary tool.

What we expect from you:

Core stack — Backend:

• Python — FastAPI, SQLAlchemy, Alembic.
• PostgreSQL + PostGIS — spatial data modelling, multi-tenant isolation at the database, session, and repository layers.
• Redis — async task queues.
• Unit & integration tests — Pytest.

Core stack — Frontend:

• React + TypeScript — strict typing; comfortable with generics, discriminated unions, and type inference.
• Next.js — App Router, Server Components, server-side data fetching, middleware, Server Actions.
• Zustand — store design, selectors, SSR hydration concerns.
• Unit & integration tests + E2E — Vitest, Cypress or equivalent.

AI / Agent Layer:

• LLM-powered agents — SSE, tool-use patterns, prompt engineering.
• Observability & evaluation — structured tracing of agent runs and tool calls, usage tracking, analytics.

Infrastructure:

• AWS — ECS, S3, containerized deploys.
• Terraform — infrastructure as code.
• CI/CD — GitHub Actions.

Engineering Practices:

• Static typing — Pyright as CI gate; type hints on every signature, no escape hatches.
• Multi-tenancy — tenant isolation at the database, session, and repository layers.
• Git discipline — conventional commits, granular atomic history, clean PRs.

Tooling — AI-assisted development (Claude Code):

AI is a primary, everyday tool on this project, not an occasional helper. We expect a candidate who has moved well beyond ad-hoc prompting and treats their AI setup as part of their engineering craft.

• AI-native development — non-negotiable. Daily, extensive, fluent use of Claude Code (or equivalent) for reading, writing, refactoring, debugging, and navigating a large codebase. Working without AI in the loop is not how this team operates.
• Plain prompting is not enough. "Ask ChatGPT a question and paste the answer" is the baseline we expect candidates to be past. We're looking for someone who engineers their AI workflow: rich structured context, project conventions and reference examples fed to the model, clear scoping, iteration, and rigorous review of generated output.
• Skills, Hooks, Subagents— must understand, must be able to build, and already uses in real work. The candidate should be able to clearly explain what each is, and — critically — has already created and uses them day-to-day, not just heard of them:
  • Skills — reusable, on-demand instruction/knowledge packages that extend the agent's competence for a given task type (e.g. scaffold a module, generate a PR, run a review) and encode the team's conventions.
  • Hooks — deterministic, automated triggers on lifecycle events (pre-commit, before/after edit, on stop, etc.) executed by the harness itself — e.g. auto-running lint/typecheck/tests on changed files, or guarding protected areas.
  • Subagents — delegating multi-step or parallel work (large refactors, audits, codebase research) to scoped agents, with verification of their output.
• MCP awareness — understands the Model Context Protocol and how to connect AI to real context and tools (codebase, Git, issue tracker, browser, observability) rather than working blind. Hands-on MCP usage is a strong plus.
• Structured change management — artifact-driven workflow from exploration through implementation to verification.
• Critical, not credulous — reviews everything the model produces, never commits code they don't understand, knows where AI accelerates the work (boilerplate, tests, scaffolding, exploration) and where it does not (complex domain/architecture logic). AI is a force multiplier for their own thinking, never a copy-paste crutch.

Soft skills / Mindset:

• Initiative — proactively asks questions, proposes solutions, flags issues without being asked.
• Software Engineering Mindset — understands fundamentals, not just framework recipes; can reason about problems from first principles.
• Comfortable with the unknown — says "I don't know, let me find out," explores and digs in.
• Thinks aloud — communicates where they are, what they're considering, what they're unsure about.
• Fast — moves quickly without sacrificing quality; comfortable with startup pace and shifting priorities.
• Collaborative but opinionated — contributes ideas, challenges assumptions respectfully.
• English — B2+ — daily verbal and written communication with the client and their team; working proficiency minimum.

What We Offer:

• Work at a Top-employer company (according to DOU 2025).
• A strong culture built on empathy, trust, openness, and real care for employees.
• Competitive compensation with regular reviews.
• Paid vacation and sick leave.
• Medical insurance.
• Personal learning budget and access to top HR tools, platforms, and practices.
• Team events and regular team-building activities.
• Flexible hybrid work model with an office in central Kyiv.