Overview:

You will join a newly created 5-person squad responsible for maintaining and evolving a portfolio of production AI agents serving the back office of the securities services (custody) division of a major European bank. The agents are built in Python on CrewAI, with a planned migration to an internal SDK and LangGraph. Surrounding APIs are written in Go. Agents are containerized (Docker) and deployed on Kubernetes; the team owns runtime configuration through ConfigMaps.

What will you do?

• Design, build, and continuously improve the AI agents themselves: their reasoning flows, tool use, prompts, and evaluation. Adapt central general-purpose agents (drafting, summarization, process driving) to custody back-office use cases.
• Develop and maintain agents in Python using LangGraph, LangChain and the internal SDK.
• Design agent workflows: task decomposition, tool/function calling against APIs, memory and state handling, guardrails, and human-in-the-loop checkpoints appropriate to back-office controls.
• Engineer, test, and version prompts; manage prompt/config changes through Kubernetes ConfigMaps with proper release discipline.
• Build and run agent evaluation: golden datasets from real back-office cases, regression suites, quality metrics (accuracy, groundedness, escalation rate), and shadow testing before rollout.
• Localize central agents: analyze the gap between general-purpose behavior and local business requirements (formats, vocabulary, workflows, controls) and implement the adaptation layer.
• Instrument agents for observability: structured logging of reasoning traces, token usage, latency, and failure modes.
• Handle model lifecycle concerns: model/provider changes, context window constraints, cost/latency trade-offs.
• Work daily with the Business Analyst to translate operational knowledge (settlements, corporate actions, client queries, reconciliations) into agent behavior.

What are we looking for?

• 3–6 years of Python engineering, with at least 1–2 years on LLM/GenAI applications.
• Practical experience with at least one agent framework (CrewAI, LangGraph, LangChain, AutoGen, or similar); understanding of ReAct-style loops, tool calling, and structured outputs.
• Solid grasp of prompt engineering, RAG patterns, and LLM evaluation techniques.
• Comfortable consuming REST/gRPC APIs (Go backend); JSON schema design; async Python.
• Working knowledge of Docker and Kubernetes basics (enough to deploy, read logs, and edit ConfigMaps safely).
• Testing culture: pytest, mocking LLM calls, deterministic test design for non-deterministic systems.
• Fluent in English

What can you expect from us?

• A permanent job contract for a long term project;
• Tech equipment + SIM Card + personal smartphone;
• Health and Life Insurance;
• Social events and team buildings;
• The commitment of letting you grow with us, and be rewarded accordingly;
• A dynamic and young team that will be always there to support you;
• Training in the latest technologies;
• Coffee, fruits, snacks and a warm welcoming when you pass by the office.