• Design, develop, and deploy AI-powered applications and intelligent services • Build and optimize solutions using Large Language Models (LLMs) • Develop Retrieval-Augmented Generation (RAG) pipelines and AI workflows • Design and implement AI agents and orchestration frameworks • Develop backend services and REST APIs supporting AI applications • Integrate AI capabilities into enterprise software solutions • Collaborate with cross-functional teams to define and deliver AI use cases • Optimize the performance, scalability, and reliability of AI solutions • Contribute to technical architecture, documentation, and engineering best practices • Stay up to date with emerging AI technologies and industry trends

• Commercial Expansion & Upsell Hunting (50%): Monetise Grassroots Love, Own the Net Revenue Retention (NRR) Quota, Execute Strategic Cross-Sells. • High-Impact Customer Success & Training (50%): Drive Platform Obsession, Prevent Churn with Data, Frontline Support & Asset Creation.

• Develop and maintain scalable back-end services using FastAPI and other modern frameworks. • Ensure seamless integration with front-end applications and external services through well-designed RESTful APIs. • Maintenance of integration modules in Node.js/React that connect the frontend to Python APIs. • Contribute to Node.js backend components when required, integrating them with Python services. • Leverage cloud infrastructure (Azure Functions, Azure Storage) for hosting and scaling applications. • Implement security best practices for API authentication (OAuth, JWT) and data protection. • Work on GenAI-driven applications, utilizing frameworks such as LangChain, LlamaIndex, vector databases, and agentic frameworks. • Containerize applications using Docker for environment management.

• Design and build LLM-powered features and AI agent pipelines — from prompt to production • Extend and optimize our RAG / knowledge base system: chunking strategies, embedding models, vector search, hybrid retrieval (BM25 + semantic), and reranking • Implement agentic workflows with tool use, multi-step reasoning and structured outputs • Build document- and email-processing pipelines that leverage LLMs for classification, extraction and summarization • Integrate and manage LLM providers (OpenAI, Anthropic, etc.) and AI SDKs across the stack • Design evaluation frameworks and feedback loops to measure and continuously improve AI output quality • Build and optimize agents for our growing template library • Operate in an AI-first development setup powered by Claude/Cursor