The Position

This is a pre-seed stage AI Fintech startup. We are looking for an intern to help build a RAG Application and LLM Connectivity.

At Factorise you get the chance to go beyond talking, studying, reading and theorizing about AI – you get to build AI applications. This is your chance to prove what you can do with AI, programming and mathematics. You will no longer be a passive recipient merely consuming influencers relentless AI gossip. You will be involved in coding, choosing the right components, providers, architecture and engineering the application. You will be part of a team and company having an actual impact on the world. The AI technology you build will make a difference for the user.  

You get the advantage to get in early in an AI startup. This means opportunity for you – if you are excellent at the job.

The Team

The team will support you with resources, learning and experience. There will be room for you to develop and grow.

The team have distinct results, experience and achievements in AI, Cloud, Learning and Software Development. This includes US Big Tech engineering positions, learning, knowledge production and communication, education, user centricity and visionary leadership. Leading technology teams, 10x growth and scaling companies, international AI application building as well as funding stages. This involves initial seed funding phase, multiple funding rounds and successful multimillion Series B funding.

About you

You can be a student, more experienced and in between projects, and/or wanting in on an exciting opportunity, while you are working your regular job.  

It’s crucial that you can code excellent in Python (and other languages) and you have the mindset of a software builder and engineer, always solving problems. It’s cool if you are from the Kolding/Vejle/Aarhus area but we can otherwise still do the work hybrid or remote. You can be self-taught, from a theoretical computer science or more trade operational type computer education – it does not matter. We look at what you can build. Mastery. You need to take pride in and commit 100% to this aim. We don’t care about your age, gender, status, network, or appearance etc. What matters is your contributions to creating the tech.

We will give you a great use case, recommendations, growth possibilities and learning trajectory. The job is unpaid and without equity for now, but when we get funded, you will have a chance to apply and negotiate for a paid position. You will need to sign an NDA.  

We make the contract for 3-6 months with a 14-day trial period. Hours a week will depend upon individual agreement, but there will be a lower limit, for you to be able to deliver the results needed.

Technical Skills

1. Natural Language Processing (NLP): Understanding of NLP concepts and techniques, such as tokenization, stemming, lemmatization, and sentiment analysis, is crucial for working with LLMs.

2. Machine Learning Fundamentals: A solid grasp of machine learning concepts, including supervised and unsupervised learning, neural networks, and deep learning frameworks like TensorFlow or PyTorch.

3. Data Handling: Experience with data preprocessing, cleaning, and augmentation techniques to prepare datasets for training and evaluation.

4. API Development: Knowledge of creating and managing APIs, necessary for integrating different components or accessing external data sources.

5. Cloud Computing: Familiarity with cloud services (AWS, Google Cloud, or Azure) for deploying models, storing data, and leveraging cloud computing resources.

Search Algorithms and AI Frameworks

1. Understanding of Search Algorithms: Since RAG involves retrieval-based components, knowledge of search algorithms and information retrieval techniques is beneficial.

2. Experience with Specific AI Frameworks: Familiarity with frameworks or libraries specifically designed for working with LLMs, such as Hugging Face’s Transformers.

Project Management

1. Agile Methodologies: Knowledge of agile development practices could be useful for iterating quickly on project deliverables.

2. Version Control: Proficiency in using version control systems, such as Git, for managing code changes and collaborating with others.

Soft Skills

1. Problem-Solving: Ability to tackle complex problems and break them down into manageable tasks.

2. Creativity: Being innovative in finding solutions or improvements to the AI model's performance.

3. Collaboration and communication: Ability to work well in a team and collaborate across different domains.

4. Adaptability: Openness to learning new technologies and approaches as the project evolves.

Factorise

We are developing a financial ecosystem to democratize the stock market. We are a pre-seed stage AI Fintech startup. We are part of the Copenhagen Fintech Community as well as the Aarhus startup hub "The Kitchen". You have the opportunity to leverage these network resources and meet with a variety of entrepreneurial people.

Check out my LinkedIn profile and connect or follow for more: www.linkedin.com/in/peter-nørgaard-04773167.

Vision:

We envision a future, where financial well-being and stock market opportunity exists in a world of abundance and equality of opportunity for all. For that to happen private investors need, among other things, the right tools to information access and curation. We envision a world where it is just as normal for private citizens to invest in shares as it is for them to shop online.

Mission:

We provide an Investment Ecosystem containing of a GenAI Chatbot, Learning Platform & Community. We empower and enable private investors decision making and action process by providing the right information at the right place. We empower investors with technology-driven investment tools and community-based learning and discussion.

Purpose:

We create structure and overview in a world of financial data excess. We build investor literacy. We connect investors to exchange ideas and knowledge. We help the user through timely data selection. We live in an era of information overload but only the dawn of tools transforming that information and data availability into knowledge and value for the user.