We are looking for a Senior AI Full Stack Engineer with strong experience in building enterprise-grade Generative AI applications and modern full-stack solutions. The ideal candidate should be comfortable working across frontend, backend, AI/LLM integration, RAG, and agentic AI workflows. This role requires someone who can take AI features from concept through development, testing, deployment, and production. Strong hands-on experience with React, TypeScript, Python, APIs, vector databases, and LLM platforms is expected.

Design and develop scalable full-stack applications using React, TypeScript, Python, and modern API technologies. Build AI-powered applications using LLMs, Retrieval-Augmented Generation, embeddings, vector databases, and enterprise search capabilities. Develop agentic AI workflows using tool calling, function calling, conversational memory, and multi-step AI orchestration. Create responsive AI user experiences including chat interfaces, streaming responses, real-time updates, and human-in-the-loop workflows. Design and integrate secure REST, GraphQL, or WebSocket APIs with databases, messaging systems, and external enterprise applications. Implement AI application security practices including prompt-injection protection, PII handling, access controls, content filtering, and audit logging. Collaborate with product, engineering, and architecture teams to convert business requirements into production-ready AI solutions. Take ownership of features across development, automated testing, CI/CD, deployment, monitoring, troubleshooting, and continuous improvement.

Strong hands-on experience with React and TypeScript, with the ability to build complex and interactive enterprise-grade user interfaces. Strong Python development experience with the ability to design backend services, APIs, and complete application workflows. Proven experience building production-grade Generative AI applications using LLMs such as OpenAI, Azure OpenAI, Anthropic, or equivalent platforms. Hands-on experience with RAG, embeddings, semantic search, vector databases such as Pinecone, Qdrant, Weaviate, or pgvector. Practical experience developing AI agents, agentic workflows, tool/function calling, conversational applications, and LLM orchestration. Strong understanding of REST, GraphQL, WebSockets, PostgreSQL, MongoDB, Redis, and event-driven or asynchronous application architectures. Good understanding of AI security practices including prompt injection, PII protection, content moderation, guardrails, authentication, authorization, and audit logging. Experience with cloud platforms, CI/CD, automated testing, containerization, and deploying and supporting AI applications in production.