Full Stack Developer

Full Stack Developer

AI-Augmented Product Engineering

Experience: 4+ Years

Type: Full-Time

Location: Remote

We are hiring a Full Stack Developer who builds product features end-to-end — from database schema to pixel-perfect UI — and brings genuine fluency in AI: not just using AI coding tools, but understanding how large language models, embeddings, and inference pipelines work well enough to build intelligent product experiences around them.

You will own feature delivery in a product-led team that ships directly to users. You’ll partner with product managers on prioritization and trade-offs, work alongside applied AI/ML engineers to integrate model capabilities into the product surface, and take ownership of outcomes — not just output. We expect product-company DNA: user empathy, metrics-driven thinking, and a bias toward shipping over spec’ing.

You will also be expected to use AI coding tools (GitHub Copilot, Claude Code, Cursor) as a core part of your development workflow — not as autocomplete, but as a force-multiplier for design, implementation, testing, and debugging.

•     Ship features end-to-end: design APIs, build React/TypeScript frontends, write backend services, own deployment through CI/CD to cloud.

•     Build AI-powered product experiences: work with AI/ML engineers to surface model outputs (recommendations, summaries, classifications, conversational features) through well-designed UIs — understanding the nuances of latency budgets, non-deterministic outputs, fallback strategies, and prompt design.

•     Operate AI-augmented dev workflows: use Copilot, Claude Code, and Cursor for code generation, refactoring, test scaffolding, and PR review — with the judgment to validate, discard, or reshape AI output.

•     Own cloud infrastructure: provision and manage services on AWS/GCP/Azure (compute, storage, messaging, CDN), configure IaC (Terraform/CDK), handle zero-downtime deployments.

•     Drive technical quality: write meaningful tests (unit, integration, E2E), maintain observability (logging, tracing, alerting), participate in architecture reviews and incident response.

•     Think like a product owner: prioritize ruthlessly using data, advocate for the user in technical decisions, and measure what you ship against real outcomes.

Core Stack:   React, TypeScript, Node.js — strong in at least two, functional across all three.

•     3+ years of professional software engineering with demonstrable ownership of shipped, user-facing product features. We care about maturity and impact, not tenure — if you’ve done meaningful work in less time, apply.

•     Background in product companies (not services/outsourcing), with evidence of product thinking: you’ve owned metrics, influenced roadmaps, made scoping trade-offs with PMs, or driven features from user insight to production.

•     Built features at the intersection of product and AI/ML — consumed LLM or ML model outputs in production UIs, designed API contracts for AI services, integrated RAG pipelines or embedding-based search, or handled the UX complexity of non-deterministic model responses.

•     Hands-on, daily use of AI coding assistants (Copilot, Claude Code, Cursor, Cody) for 6+ months — you can articulate how these tools change your workflow, where they fail, and how you validate AI-generated code.

•     Production cloud experience (AWS, GCP, or Azure): deployed services, configured CI/CD, managed infrastructure-as-code, debugged production incidents — not just local Docker.

•     Solid web fundamentals: REST/GraphQL API design, auth patterns, state management, performance optimization, accessibility.

•     Experience with relational and/or document databases (PostgreSQL, MongoDB, DynamoDB) — schema design, indexing, query optimization.

•     Deep AI fluency: you understand tokenization, context windows, temperature, fine-tuning trade-offs, and embedding similarity — not just API calls, but the mechanics that shape product decisions.

•     Maintained a prompt engineering pipeline, evaluation harness, or RAG system in production.

•     Can show concrete before/after evidence of how AI tools changed your velocity or code quality (PR throughput, test coverage, refactoring scope).

•     Experience with event-driven architectures, message queues (Kafka, SQS, RabbitMQ), or real-time pipelines.

Frontend	React, TypeScript, Next.js / Vite, Tailwind CSS
Backend	Node.js (Express / Fastify / NestJS), Python (FastAPI) for AI services
Data	PostgreSQL, Redis, DynamoDB / MongoDB, vector DBs (Pinecone / pgvector)
Cloud & Infra	AWS / GCP / Azure, Docker, Kubernetes, Terraform / CDK
AI Tooling	GitHub Copilot, Claude Code, Cursor; LLM APIs (Anthropic, OpenAI)
DevOps	GitHub Actions / GitLab CI, Datadog / Grafana, Jest, Playwright

We do not accept self-reported “I use Copilot” at face value. During interviews, candidates will:

•     Complete a live coding exercise using their preferred AI assistant — we observe how you prompt, iterate, validate, and override suggestions.

•     Walk through a recent PR where AI tools contributed materially — what was generated, modified, and rejected.

•     Discuss failure modes: when AI tools gave incorrect or insecure suggestions and how you caught them.

This is a product engineering role for builders who leverage AI as a force-multiplier. If that describes you, we want to talk.