Data Scientist - MID Profile - BN 12/11/26

Context: 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. Expectations: - 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. - General Stack: Google ADK, LangGraph, DSPy, vLLM, FastAPI, Python, Pytest. Arize Phoenix. Kubernetes, Jenkins, GPUs. Responsibilities: - Design and develop LLM-based agentic solutions to automate complex business processes. - Work closely with business teams to identify needs and drive the adoption of AI solutions. - Develop data extraction pipelines and implement evaluation and observability frameworks for AI agents. - Deploy and industrialize models (LLMs, GPUs), ensuring quality through testing and DevOps best practices - You are taking full ownership in driving AI features. - You remember to respect the boundaries and non-deterministic nature of LLMs, communicate constraints clearly, and build robust safety rails. - You are a team player, communicate technical trade-offs cleanly with architects and business users, and enjoy mentoring peers. - You are testing ideas and PoCs quickly, validate feasibility with data and avoid technical debt. Career Prospects: - Build expertise in Agentic AI architectures and the industrialization of LLMs in production. Work on high-visibility projects within the Investment Banking division.