Staq is a leading Banking-as-a-Service (BaaS) and embedded finance platform, transforming the way businesses integrate banking and financial services. At Staq, we empower our clients to innovate, expand, and streamline their financial services offerings, leveraging our cutting-edge platform. Our mission is to bridge the gap between traditional banking and the digital era, providing seamless, scalable, and secure financial solutions.

Es posible que un gran número de candidatos se presenten a este puesto, así que asegúrese de enviar su CV y su solicitud lo antes posible.

The Role

We are building the intelligence layer that will power an AI-powered financial assistant and serve as the SDK that other banking applications plug into. The long-term vision is an AI-native bank where every customer interaction, recommendation, and financial operation is orchestrated through this platform. That means the agent runtime, automation engine, recommendation systems, and tool execution framework all need to be built as reusable, production-grade infrastructure — not one-off features for a single product.

The objective is to build, harden, and ship the intelligence platform across multiple products simultaneously. You will be building the systems that make AI actually work in finance: agents that reason about money, automations that run reliably on people’s financial data, recommendations that are genuinely useful, and tool execution that is safe and observable. This is systems engineering meets applied AI.

Key Responsibilities

Agent Runtime & Orchestration

Build and maintain production AI agent flows using Python and LangGraph, including multi-step planning, tool selection, and context assembly

Author and evolve Agent Cards that define agent capabilities, context requirements, and output contracts for each product domain

Implement the agent-side integration with Temporal workflows — the AGENT_STEP and AGENT_LOOP activity interfaces that the Java orchestrator calls into

Own prompt engineering, template management, and context window optimization across all agent flows

Design and implement memory systems that give agents meaningful continuity — conversation history, user financial context, and long-term preference tracking across sessions

Design and implement automation flows that go beyond conversational agents — scheduled financial health checks, proactive alerting, background data analysis, and event-driven triggers

Build reliable,



deterministic automation pipelines that can execute multi-step financial operations with proper error handling, compensation logic, and human-in-the-loop escalation

Ensure automations are idempotent, observable, and operate within the platform’s risk gate framework

Recommendation Systems

Build and iterate on recommendation engines that surface personalized financial insights, product suggestions, and actionable next-best-actions to users

Design the data contracts and feature pipelines that feed recommendations, working with domain services for banking, credit, and subscription data

Implement evaluation frameworks to measure recommendation quality, relevance, and user engagement

Sandboxed Tool Execution

Own the integration with sandboxed execution environments (E2B) where agents run tools against real financial APIs and data sources

Implement and maintain MCP (Model Context Protocol) tool definitions, ensuring agents can safely invoke financial operations within policy-controlled boundaries

Build guardrails around tool execution — input validation, output verification, and safe fallback behavior when tools fail or return unexpected results

Reliability & Testing

Build comprehensive test harnesses for agent behavior — deterministic scenario tests, regression suites, and evaluation benchmarks

Own the reliability engineering of the agent runtime: graceful degradation when LLMs misbehave, proper retry logic, timeout handling, and circuit breakers

Support adversarial testing and red-teaming efforts from the AI side

Platform & SDK Mindset

Everything you build must be reusable. Zeen is the first product, but the intelligence layer is an SDK — other banking applications will build on top of the same agent patterns, tool integrations, and automation frameworks

Maintain and evolve the shared contracts (Agent Cards, tool schemas, risk gate interfaces) that allow new products to onboard onto the platform with minimal custom work

Think in terms of clean abstractions and extension points, not hard-coded product logic

Technical Environment

Python (primary),



with integration touchpoints to Java microservices

LangGraph for agent orchestration; Temporal Cloud (Java SDK) as the durable workflow engine

E2B sandboxed containers for tool execution; MCP for tool protocol

OpenTelemetry for observability; structured artifact logging

Fintech domain: Plaid integrations, banking/credit/subscription data

What We Are Looking For

Must Have

3+ years building production AI/ML systems (not just notebooks — deployed, monitored, maintained)

Strong Python fundamentals and experience with async patterns, error handling, and production-grade code

Hands-on experience with LLM application development — prompt engineering, context engineering, tool/function calling, and structured outputs

Experience building at least one of: recommendation systems, automation pipelines, or multi-step agent workflows

Understanding of evaluation and testing for non-deterministic systems — you know that “it works on my prompt” is not a test strategy

Comfort working with financial data where correctness and reliability matter more than speed of iteration

Strong Signals

Experience with agent frameworks (LangGraph, LangChain, AutoGen, CrewAI) in production, not just prototypes

Familiarity with memory systems for AI agents — short-term and long-term memory architectures, retrieval-augmented generation, and context window management strategies

Experience with prompt management at scale — versioning, templating, A/B testing, and systematic prompt optimization workflows

Familiarity with sandboxed code execution, MCP, or tool-use patterns for LLM agents

Background in fintech, financial data, or regulated industries

Experience with recommendation engines (collaborative filtering, content-based, hybrid approaches)

Familiarity with workflow orchestration systems (Temporal, Airflow, Prefect) and how AI fits into durable execution patterns

Experience with LLM observability and performance tracking — call latency profiling, token usage monitoring, cost attribution, and tracing through multi-step agent flows

What This Role Is Not

This is not a pure ML research position. We are not training foundation models. You will be building application-layer AI systems on top of LLMs and integrating them into a financial services platform that real people depend on for real money. xugodme The challenge is in the systems engineering, reliability, and product thinking — not in publishing papers.

📌 AI Engineer (España)
🏢 Staq.io
📍 España