What You'll Do :

- Design and build full-stack web applications end to end front-end UI, back-end services, APIs, and data layer.

- Build and consume well-structured REST (and where relevant GraphQL) APIs, with clean, documented contracts.

- Integrate LLM / AI capabilities into applications calling model provider APIs (OpenAI, Anthropic, Gemini, or Azure OpenAI), wiring up retrieval or RAG features, and handling streaming responses, prompts, and structured outputs.

- Design and work with relational and/or NoSQL databases (PostgreSQL, MySQL, MongoDB) schema design, queries, and performance.

- Own deployment : containerize with Docker and ship through Git-based CI/CD pipelines to cloud infrastructure (AWS, Azure, or GCP).

- Write maintainable, tested code and participate in code reviews.

- Collaborate with product, design, and other engineers, and communicate progress and tradeoffs clearly to non-technical stakeholders.

- Take prototypes to production quickly and reliably within the contract window.

Must-Have Skills :

- 56 years building and shipping production web applications.

- Front end : deep proficiency in at least one modern framework React, Angular, or Vue with solid HTML, CSS, and JavaScript/TypeScript.

- Back end : strong experience in at least one server stack Node.js, Python (Django / FastAPI / Flask), or .NET / C#.

- REST API design and integration.

- AI/LLM integration practical, hands-on experience integrating LLM or GenAI capabilities into an application (provider APIs, prompt design, handling model responses in production).

- Databases PostgreSQL, MySQL, or MongoDB, including schema design and query optimization.

- Git and version-control workflows.

- Docker and CI/CD pipelines.

- A strong grasp of software fundamentals clean code, testing, and reproducible deployments.

- Comfort working independently on ambiguous problems and delivering to a deadline.

Good to have :

- TypeScript across the full stack.

- RAG pipelines, vector databases (Pinecone, Weaviate, Chroma, FAISS, Qdrant), or embeddings / semantic search.

- Agent frameworks or tool-calling (LangChain, LangGraph).

- Cloud platforms at depth (AWS, Azure, or GCP) and infrastructure-as-code.

- Real-time features (WebSockets, streaming), message queues, or caching (Redis).

- Authentication / authorization (OAuth2, JWT, SSO) and security best practices.

- Automated testing frameworks and test-driven development.

- Working in a regulated or compliance-sensitive domain.