Descripción del trabajo
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.
#J-18808-Ljbffr