Explicitly requires vibe coding skills and heavy use of AI-assisted tools (Cursor, Claude Code, Copilot) to rapidly prototype and ship production features.
About the Role
Lead design and implementation of AI-native, full‑stack enterprise applications, owning end-to-end features from frontend to backend and AI agent integrations. Drive architecture and best practices for scalable, secure, and observable AI services while mentoring engineers and shipping production-grade systems.
Job Description
Role
As a Senior/Staff Full Stack AI Engineer, you will design, build, and enhance AI-native enterprise applications and platform components. You will take end-to-end ownership of features spanning pixel‑perfect React frontends, backend microservices, data/search systems, and AI agent pipelines. You will drive architecture decisions, champion DevOps/AIOps practices, and mentor other engineers.
Key Responsibilities
- Drive architecture and technical planning for new features and services end-to-end.
- Design and develop high-performance, scalable cloud-first services and microservices.
- Build and ship complex multi-service features with minimal oversight.
- Mentor and elevate engineers through tooling, best practices, and code/architecture reviews.
- Design patterns for AI agent orchestration, data pipelines, and micro-frontends.
- Implement robust observability, monitoring, alerting, and security/privacy controls for AI services.
- Champion platform scalability, reliability, and operability across services.
What You’ll Build
- End-to-end AI-powered products combining LLM agents, document intelligence, and knowledge graphs to automate enterprise workflows.
- Rich, responsive React + TypeScript frontends with real-time streaming, document viewers, code editors, and interactive visualizations.
- High-performance backend microservices (Python/FastAPI, Node.js, or Java) using async patterns and event-driven architectures (Kafka, Celery).
- LLM orchestration and agent pipelines using LangChain/LangGraph, LlamaIndex, MCP integrations, and custom agent frameworks.
- Semantic search, pgvector embeddings, knowledge graphs (Neo4j), and intelligent document processing pipelines.
Tech Stack (highlights)
- Frontend: React 18, TypeScript, Vite, Material UI, MobX/Redux, D3, Monaco Editor, Module Federation
- Backend: Python 3.11+, FastAPI, SQLModel/SQLAlchemy, Node.js, Java, Celery, Kafka, Redis, WebSockets
- AI / LLM: Anthropic Claude, OpenAI GPT, LangChain, LangGraph, LlamaIndex, MCP, Azure Document Intelligence
- Data: PostgreSQL, pgvector, Neo4j, SPARQL, Pydantic
- Cloud & Infra: Azure, AWS (S3, boto3), Docker, Kubernetes, Terraform/Ansible, OpenTelemetry, GitHub Actions
- Dev Tooling: Cursor, Claude Code, Copilot, Poetry, Ruff, pytest, Vitest, Storybook, pre-commit
Requirements
- Typically 5–8+ years of relevant software development experience with demonstrated technical leadership (at least 2 years in senior/lead role).
- Strong hands-on experience across frontend (React/TypeScript) and backend development (Python/FastAPI, Node.js, Java, Golang, or C++).
- Demonstrated experience building applications with LLM APIs and AI-assisted coding tools; familiarity with agent frameworks (LangChain, LlamaIndex) is highly desired.
- Strong cloud-native mindset: containers, orchestration (Docker, Kubernetes), and familiarity with cloud providers and IaC (Terraform/Ansible).
- Experience with async patterns, task queues (Celery), message brokers (Kafka/Redis), and event-driven architectures.
- Database fluency: PostgreSQL, ORMs (SQLAlchemy/SQLModel), and exposure to vector/graph databases.
- Modern dev practices: CI/CD, testing (pytest, Vitest), code review culture, observability, security, and privacy.
- Comfortable owning features from prototype to production across UI, API, database, and deployment.
Nice to Have
- Bachelor’s or Master’s in Computer Science or related field (or equivalent experience).
- Experience building AI-native production systems for regulated, high-stakes domains.
- Prior experience working in early-stage startups or enterprise product environments.