Sr. Full-Stack Developer

Key Responsibilities

Development

• Design, develop, and ship features across the full stack — frontend (web UI), backend services/APIs, and

database layers.

• Use AI coding tools (Claude Code, GitHub Copilot, Cursor, or similar) to accelerate development,

refactoring, and code review — while maintaining full ownership of code quality and correctness.

• Translate business requirements into technical designs, estimates, and implementation plans.
• Build and consume REST/GraphQL APIs and integrate third-party services (payments, auth, email, AI/LLM

APIs).

AI Integration

• Design and build AI-powered product features using LLM APIs (Anthropic Claude, OpenAI, or similar): chat

assistants, summarization, extraction, classification, semantic search.

• Apply prompt engineering, RAG (retrieval-augmented generation), embeddings/vector search, and

function/tool calling where appropriate.

• Evaluate AI feature quality: measure accuracy, handle hallucinations, add guardrails, and manage

cost/latency trade-offs.

Testing & Quality

• Own quality for the features you build: write unit, integration, and end-to-end tests — and use AI tools to

generate and expand test coverage efficiently.

• Define test plans and acceptance criteria; perform functional, regression, and performance testing before

releases.

• Set up and maintain automated test suites in CI/CD pipelines.
• Reproduce, triage, and root-cause bugs from QA and production, using AI-assisted debugging where it

speeds up resolution.

Maintenance & Production Support

• Maintain and enhance existing production applications — bug fixes, security patches, dependency

upgrades, performance tuning.

• Monitor production systems (logs, metrics, alerts), diagnose incidents, and drive them to resolution.
• Perform database maintenance: schema migrations, query optimization, data repair/backfill scripts.
• Refactor legacy code and reduce technical debt without disrupting live users.

Full Product Lifecycle Ownership

• Participate in all phases: discovery, requirements, architecture, development, testing, deployment, release,

and post-release support.

• Work with product managers, designers, and stakeholders to shape scope and priorities.
• Plan and execute releases, including rollback strategies and zero-downtime deployments.
• Contribute to documentation: technical specs, runbooks, API docs, release notes.
• Mentor junior developers, including on effective and responsible AI tool usage.

Required Skills & Experience

• 6+ years of professional software development experience, with at least 2 full product lifecycles taken from

concept to production and ongoing maintenance.

• Proven daily use of AI development tools — Claude Code, GitHub Copilot, Cursor, Windsurf, or similar — in

real project work; able to describe concretely how AI tools changed your workflow and where their limits

are.

• Hands-on experience integrating LLM APIs (Anthropic, OpenAI, Gemini, or open-source models) into at

least one shipped project or feature.

• Strong proficiency in JavaScript/TypeScript and a modern frontend framework (React, Vue, or Angular).
• Strong backend experience with Node.js (or Python/Java/.NET) — APIs, services, background jobs.
• Solid command of SQL databases (PostgreSQL/MySQL): schema design, indexing, migrations, query

optimization. Exposure to NoSQL/vector databases (pgvector, Pinecone, Redis) is a plus.

• Hands-on testing experience: unit/integration frameworks (Jest, Vitest, Playwright/Cypress) and test

automation in CI.

• Experience with Git, CI/CD pipelines, and cloud platforms (AWS/Azure/GCP).
• Production operations experience: monitoring, logging, live debugging, incident response.
• Understanding of security fundamentals — including AI-specific risks (prompt injection, data leakage to

third-party models).

• Strong communication skills — able to explain technical trade-offs to non-technical stakeholders.

Nice to Have

• Experience building agentic workflows (multi-step tool use, MCP servers, agent frameworks/SDKs).
• RAG pipeline experience: chunking, embeddings, vector search, re-ranking.
• Experience with Docker/Kubernetes and infrastructure-as-code.
• Experience with serverless platforms or BaaS (Supabase, Firebase).
• Data migration experience — moving customers from legacy systems into a new product.
• Prior work on SaaS or multi-tenant products.

What Success Looks Like

• Features ship on time, are well-tested, and rarely bounce back from QA or production.
• You ship measurably faster than a traditional workflow by leveraging AI tools — without sacrificing code

quality or review rigor