About the role
AI summarisedDesign and build end-to-end, AI-powered applications with a focus on rapid MVPs and production-ready agentic experiences. This role involves working across the entire stack—from LLM integration and RAG pipelines to APIs and front-end UIs—to deliver high-impact generative AI solutions for clients.
ConsultingOnsiteOther Functions
Key Responsibilities
- Build full-stack MVP and production-quality applications utilizing agentic AI patterns and multi-agent frameworks.
- Design and implement LLM-based features, including prompt engineering, tool usage, and Retrieval-Augmented Generation (RAG) pipelines.
- Develop backend services and APIs using Python and FastAPI, integrating with vector databases and external tools.
- Create intuitive front-end user interfaces using React or Next.js to deliver compelling agentic experiences and demos.
- Implement and configure multi-agent workflows using LangGraph and related frameworks, including routing, tool integration, and coordination.
- Build reusable components, libraries, and templates to accelerate future GenAI and agentic builds.
- Collaborate closely with product managers, designers, and AI engineers to deliver end-to-end features from concept to deployment.
Requirements
- Strong experience in full-stack development using Python for backend and React/Next.js for frontend.
- Hands-on experience with LLM integration, prompt engineering, and RAG pipeline implementation.
- Familiarity with agent frameworks and orchestration tools such as LangChain, LangGraph, or similar multi-agent systems.
- Experience building APIs and microservices with FastAPI or equivalent frameworks.
- Knowledge of containerization and modern delivery practices (Docker, Git, CI/CD).
- Experience working with vector databases and integrating them into AI applications.
- Strong debugging skills and a track record of rapid prototyping for MVPs and demos.