Quantum is a global technology partner delivering high-end software products that address real-world problems.
We advance emerging technologies for outside-the-box solutions. We focus on Machine Learning, Computer Vision, Deep Learning, GIS, MLOps, Blockchain, and more.
Here at Quantum, we are dedicated to creating state-of-the-art solutions that effectively address the pressing issues faced by businesses and the world. To date, our team of exceptional people has already helped many organisations globally attain technological leadership.
We constantly discover new ways to solve never-ending business challenges by adopting new technologies, even when there isn’t yet a best practice. If you share our passion for problem-solving and making an impact, join us and enjoy getting to know our wealth of experience!
About the position
Quantum is expanding the team and has a brilliant opportunity for a Middle Data Science Engineer. The client is a pioneering technological research company that leverages proprietary AI-based analysis and language models to provide comprehensive insights into global stocks in multiple languages. Its mission is to bridge the knowledge gap in the investment world and empower investors to become “super-investors.” Through generative AI technology integrated into brokerage platforms and financial institutions’ infrastructures, the company provides instant fundamental analyses of global stocks and enables informed investment decisions for millions of investors worldwide.
As a Middle Data Science Engineer, you will research, build, and deploy machine learning models and model-driven services powering an AI-driven financial platform. You will take end-to-end ownership of solutions, from problem framing and data exploration through modeling and evaluation to delivering production-ready services, with a strong focus on LLMs, NLP, and AI-powered solutions.
Must-have skills:
3+ years of commercial experience in Data Science, Machine Learning, or AI Engineering
Strong Python skills, OOP, and software engineering fundamentals
1+ year of hands-on experience with LLMs and NLP
Proficiency in prompt engineering
Good understanding of modern Deep Learning architectures
Hands-on experience with at least one Deep Learning framework (PyTorch, TensorFlow)
Experience working with SQL and NoSQL databases
Practical experience with Docker and containerized applications
Practical experience with AI coding agents and development tools (e.g. Claude Code, Cursor)
At least Upper-Intermediate level of English (spoken and written)
Would be a plus:
Hands-on experience with RAG, vector databases, and semantic search
Experience with LLM evaluation
Experience with fine-tuning or training LLMs
Experience with Cloud Computing Platforms (AWS, GCP, Azure)
Experience with LLM/AI frameworks such as LangChain, LangGraph, LlamaIndex, or similar
Participation in Kaggle competitions
Your tasks will include:
End-to-end development of AI and Data Science solutions, from problem framing and data exploration to production deployment
Design and development of LLM-powered conversational features, including multi-turn dialogue, context handling, and answer generation over financial data
Development and maintenance of RAG pipelines over financial documents and data
Applying prompt engineering, structured outputs, tool use, and agent workflows to LLM-powered solutions
Training, fine-tuning, and maintenance of NLP models for tasks such as intent detection, classification, entity extraction, and embeddings
Preparing and labelling training data and continuously improving models with new data and user feedback
Development and maintenance of Python services within LLM-powered microservice systems
Building reliable LLM workflows with model routing, fallbacks, retries, caching, and rate-limit handling
Designing and developing RESTful APIs consumed by internal services, frontends, and partners
Owning features end to end, from technica
