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.