MID Data Scientist

A team responsible for the development and industrialization of applications for the Investment Banking division. Accelerating the integration of generative AI solutions and AI agents into business processes. A strategic environment within the platform, with high-impact use cases in document analysis, credit, and finance.

Requirements description

Strong Python with extensive experience in Object-Oriented Programming and enterprise software design patterns.

Proven track record building asynchronous microservices and endpoints using FastAPI.

Hands-on practice in pytest (unit, functional, and integration testing for ML/LLM pipelines).

Bash scripts for automation, data handling, and container interaction.

LLMs architecture understanding proven by project portfolio.

Architecting multi-step, stateful agentic graphs using LangGraph or Google ADK.

Hands-on experience optimizing LLM pipelines programmatically using DSPy.

Practical knowledge of deploying and running high-throughput open-weight models on GPU infrastructure using runtimes such as vLLM (experience with CUDA and fine-tuning is a strong plus).

Hands-on familiarity with automated build, test, and release pipelines (Jenkins).

Experience setting up tracing, drift detection, and automated evaluations for generative workflows using tools like Phoenix Arize.

Dedication to shipping stable, secure code into flagship banking infrastructure where compliance, auditability, and data security are non-negotiable.

Gene