Lead FullStack Engineer
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Key skills for this role
About the Role
CNTXT AI is seeking a seasoned AI Full Stack Tech Lead to own the technical direction of the product engineering team. The role involves architecting backend systems in Node.js, shipping full-stack features with React/Next.
Key Skills for This Role
Responsibilities
- Design and own scalable backend systems using Node.js (REST APIs, microservices, event driven architecture)
- Build Python based services for data pipelines, ML integration, and automation workflows
- Architect database schemas and storage strategies across SQL and NoSQL
- Own observability: structured logging, monitoring, alerting, and performance profiling
- Drive CI/CD pipelines, infrastructure as code, and DevOps best practices
- Build and review frontend features using React/Next.js and TypeScript
- Define frontend architecture standards: component design, state management, API integration patterns
- Integrate LLM APIs into product features
- Design and implement RAG pipelines, prompt engineering workflows, and AI powered automation
- Lead a cross functional team of backend, frontend, and AI engineers
- Conduct code reviews, architectural reviews, and technical design sessions
- Mentor engineers and define team engineering standards and growth paths
Requirements
- 6+ years of Node.js experience at expert level
- Strong TypeScript production experience
- Experience with REST API and GraphQL design
- Proficiency in PostgreSQL/MySQL schema design and tuning
- Experience with cloud native services (AWS/GCP/Azure)
- Experience with CI/CD pipelines and IaC
- Experience with AI coding tools (Copilot, Cursor, etc.)
- System design and architectural decision making skills
- Cross functional team leadership experience (5+ engineers)
- LLM API integration experience (OpenAI/Anthropic)
- Docker and Kubernetes experience
- MongoDB/Redis and caching strategies
Full Job Posting
About the Role
- We are looking for a seasoned AI Full Stack Tech Lead to own the technical direction of our product engineering team.
- This role demands both depth and breadth: architect and build backend systems in Node.js, ship full stack features using React/Next.js, integrate AI capabilities, and lead a cross functional team.
- You bring a strong AI first mindset and actively leverage AI coding tools to elevate team velocity.
Key Responsibilities
- Design and own scalable backend systems using Node.js — REST APIs, microservices, event driven architecture.
- Build Python based services for data pipelines, ML integration, and automation workflows.
- Architect database schemas and storage strategies across SQL (PostgreSQL) and NoSQL (MongoDB, Redis).
- Own observability: structured logging, monitoring, alerting, and performance profiling in production.
- Drive CI/CD pipelines, infrastructure as code, and DevOps best practices across the team.
- Build and review frontend features using React / Next.js and TypeScript.
- Define frontend architecture standards: component design, state management, API integration patterns.
- Integrate LLM APIs (OpenAI, Anthropic, open source models) into product features.
- Design and implement RAG pipelines, prompt engineering workflows, and AI powered automation.
- Lead a cross functional team of backend, frontend, and AI engineers — day to day technical direction.
- Conduct regular code reviews, architectural reviews, and technical design sessions.
- Mentor engineers at all levels; define team engineering standards and growth paths.
Required Skills & Experience
- Node.js — 6+ years, expert level
- TypeScript — strong, production experience
- REST API & GraphQL design
- PostgreSQL / MySQL — schema design & tuning
- AWS / GCP / Azure — cloud native services
- CI/CD — GitHub Actions, pipelines, IaC
- AI coding tools — Copilot, Cursor, or similar
- System design & architectural decision making
- Agile / iterative product delivery
- Cross functional team leadership (5+ engineers)
- LLM API integration (OpenAI / Anthropic)
- Docker & Kubernetes
Nice to Have
- AI/ML frameworks: LangChain, LlamaIndex, Hugging Face, open source LLMs (Mistral, LLaMA)
- Vector databases: Pinecone, Weaviate, Qdrant, or pgvector for RAG pipelines
- Data engineering: ETL pipelines, data streaming (Kafka), or time series systems
- DevOps depth: Terraform, Helm, advanced Kubernetes, or observability platforms (Datadog, Grafana)
- Product sense: Experience working on AI native or automation first products
- Open source: Active contributions to open source projects or a visible technical portfolio
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