Founding Software Engineer Expected outcomes: Design and implement production-grade AI systems with focus on LLMs and autonomous agents Develop optimized RAG systems and embedding pipelines Build robust Python applications emphasizing async programming and performance Deploy, monitor and maintain AI systems in production Establish a scalable and maintainable technical foundation, balancing speed of delivery with long-term scalability Champion 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 culture Foster a culture of learning and innovation, enabling the team to adapt to the rapidly evolving AI landscape. All the desired skills we are looking for You have experience building in a fast-iteration environment - ideally with start-up experience You solve problems quickly and enjoy delivering outcomes, not just fancy technology You love learning and being part of a high-performing team Data-first - you love thinking on a data-first mindset and its possibility to create better products, experiences and business models down the line You’ve been deploying agent architectures / LLMs in production and at scale On a technical note, our stack below - we look for people that can combine a part (or all if possible) of the technical skills defined below Python expertise Async programming, API development, real-time ASGI, Django/Channels Testing, logging, monitoring, performance optimization Production-grade application development Knowledge of data storage solutions (both SQL and NoSQL) Async message queues (Celery + RabbitMQ / Redis) Experience with high-throughput, low-latency systems LLMs usage Strong understanding of LLMs, prompt engineering, chaining, caching and model fine-tuning Experience with both open-source and closed-source LLMs Experience with Retrieval-Augmented Generation (RAG), embedding optimization, chunking strategies, vector databases (ChromaDB, Pinecone) Experience with LLM frameworks (Llamaindex / Langchain) Model evaluation metrics and performance benchmarks LLM guardrails and security best practices LLM cost-optimisation strategies