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