Founding Software EngineerExpected outcomes:Design and implement production-grade AI systems with focus on LLMs and autonomous agentsDevelop optimized RAG systems and embedding pipelinesBuild robust Python applications emphasizing async programming and performanceDeploy, monitor and maintain AI systems in productionEstablish a scalable and maintainable technical foundation, balancing speed of delivery with long-term scalabilityChampion the use of AI, staying at the forefront of Agentic AI advancements, and apply these learnings to deliver value-driven solutions.Be part of building our performance-driven culture that values transparency, simplicity, and rapid iteration - you will be part of the basis of our cultureFoster a culture of learning and innovation, enabling the team to adapt to the rapidly evolving AI landscape.All the desired skills we are looking forYou have experience building in a fast-iteration environment - ideally with start-up experienceYou solve problems quickly and enjoy delivering outcomes, not just fancy technologyYou love learning and being part of a high-performing teamData-first - you love thinking on a data-first mindset and its possibility to create better products, experiences and business models down the lineYou’ve been deploying agent architectures / LLMs in production and at scaleOn a technical note, our stack below - we look for people that can combine a part (or all if possible) of the technical skills defined belowPython expertiseAsync programming, API development, real-time ASGI, Django/ChannelsTesting, logging, monitoring, performance optimizationProduction-grade application developmentKnowledge of data storage solutions (both SQL and NoSQL)Async message queues (Celery + RabbitMQ / Redis)Experience with high-throughput, low-latency systemsLLMs usageStrong understanding of LLMs, prompt engineering, chaining, caching and model fine-tuningExperience with both open-source and closed-source LLMsExperience with Retrieval-Augmented Generation (RAG), embedding optimization, chunking strategies, vector databases (ChromaDB, Pinecone)Experience with LLM frameworks (Llamaindex / Langchain)Model evaluation metrics and performance benchmarksLLM guardrails and security best practicesLLM cost-optimisation strategies