AI/LLM Engineer

What you’ll do

You will be a key figure in our Engineering Department, responsible for operationalizing innovative methods and solutions derived from our own R&D as well as the latest AI/LLM technologies.

You will be responsible for leveraging machine learning libraries and frameworks to create and fine-tune models that can understand and produce language based on prompts.

The Engineering Department is dedicated to delivering commercial-quality solutions as the final product. While focusing on delivering these solutions, we remain committed to periodically revisiting research activities to ensure a continual synergy between our engineering efforts and ongoing research endeavors. We work both on big in-house projects as well as cooperate with partners in specific implementations. 

What you need to know

• 3 years of experience in machine learning in python
• deep understanding of neural networks with focus on generative LLM
• experience in prompt engineering
• knowledge of OpenAI REST API and their python package
• understanding of asynchronous programming paradigm

Nice to have

• experience in audio signal processing for analyzing and generating audio content. Knowledge of models that can generate speech or music would be beneficial.
• proficiency in pydantic and instructor
• NLP knowledge especially spacy/gensim packages
• understanding of backend RESTful frameworks especially fastapi
• experience with NoSQL databases especially DynamoDB
• experience with AWS machine learning and ETL stack 
• familiarity with web scraping, e.g. css selectors, html tags
• experience with Computer Vision: knowledge of CV libraries and frameworks (e.g., OpenCV, TensorFlow, or PyTorch) 
• familiarity with generative models for images and video: understanding of generative models beyond text, such as GANs and VAEs
• deep understanding of RAG architecture and experience in fine-tuning RAG models for specific domains or tasks

We offer:

• market-based salary adjusted to your skills; 
• flexible working hours; 
• 26 days of paid leave
• partially remote 
• internal trainings;
• 10% of work time for self-development;