Fetcherr is an AI-driven company specializing in deep learning, algorithmic trading, and large-scale data solutions. Our core technology, the Large Market Model (LMM), enables accurate demand forecasting and real-time, data-driven decision-making. Originally focused on the airline industry, Fetcherr is expanding its AI solutions across additional industries.

We are looking for an AI Engineer to design and implement the decision-making logic of our Price Optimizer. You will focus on translating complex business rules into mathematical models and production-ready Python code, supported by a dedicated Data Engineering team that handles the underlying infrastructure.

Your primary mission is to build the "brain" of our system. You will work at the intersection of Data Science and Product, ensuring our simulation and optimization engines accurately reflect real-world pricing strategies and market dynamics. While you will write production code, you will rely on our Data Engineers for ETL pipelines orchestration and distributed compute scaling.

Responsibilities :

• Implement Business Logic: Translate intricate pricing rules and commercial strategies into robust Python code and mathematical constraints.
• Refine Optimization Models: Develop and tune the simulation and revenue management algorithms that drive our pricing recommendations.
• Write Clean Code: Contribute high-quality, tested, and maintainable code to the core logic repositories.
• Analyze & Improve: Use data to validate model behavior and identify edge cases where business rules clash with algorithmic outputs.
• Collaborate: Partner with Solution Architects to define logic requirements and with Data Engineers to integrate your models into the Dagster pipelines.

Requirements:

You'll be a great fit if you have...

• 3+ years of experience in Data Science or Algorithmic Development with Python.
• Proven ability to translate complex business requirements into code and logical rules.
• Strong background in Mathematical Optimization, Simulation, or Logic Programming.
• Fluency in the PyData stack (Pandas, NumPy, SciPy).
• Experience writing production-quality code (not just notebooks) - you understand modular design and unit testing.