Machine Learning/AI Specialist

Join a small team (5–6 people) building and scaling data-driven trading strategies. Your primary focus will be time-series modeling for alpha discovery and robust productionization in low-latency environments. You can expect direct impact, fast feedback from live strategies, and meaningful upside.

Requirements description

Must have:

• Practical proficiency in Python.

• Ability to perform exploratory data analysis using libraries such as NumPy, pandas, and scikit-learn. Familiarity with deep learning libraries: PyTorch/TensorFlow.

• Experience with model evaluation and working with production pipelines/models.

• Basic proficiency with Git and Bash.

• Solid mathematical and statistical foundations, with a particular focus on understanding deep learning algorithms.

• Experience working with Large Language Models (LLMs—e.g., GPT, LLaMA, Qwen), including running them locally and integrating them into applications.

• Knowledge of LLM techniques (prompt engineering, RAG, response quality evaluation) and the basics of Transformer architectures.

• A degree in a quantitative/technical field or equivalent hands-on experience.

Nice to have:

• Familiarity with financial markets and related datasets.

• Knowledge of the latest deep learning techniques, especially for real-time and low-latency applications.

• Experience fine-tuning LLMs (e.g., LoRA, instruction fine-tuning) and with tools such as LangChain, LlamaIndex, Hugging Face, or Ollama.

Your responsibilities

1. Build end-to-end ML pipelines in Python

2. Run experiments and manage baselines; track results and compare variants

3. Evaluate models and assess performance

4. Analyze data; clean, preprocess, and validate datasets

5. Prepare corpora for LLM fine-tuning

6. Ship and operate low-latency inference; maintain CI/C