AI ENGINEER / AI AGENT DEVELOPER

AI ENGINEER / AI AGENT DEVELOPER QNB is always supporting employees who are eager to create a development at their career and the future of the sector. Our human resources policies are aiming to create an equal, fulfilled and empowering working atmosphere for individuals willing to have a satisfying career journey. We’re growing our AI team and looking for an engineer with hands-on experience building AI agents. You’ll work on agentic systems from design to production, together with a team that builds everything in-house. In this role, you will: Design, develop, and maintain LLM-based AI agents and multi-agent workflows that automate and enhance internal and customer-facing banking processes Architect end-to-end agentic systems, including orchestration, tool and function calling, memory, retrieval (RAG), and skill/connector integrations Hands-on experience with tool-use design, multi-agent architectures, and Model Context Protocol (MCP) Integrate AI agents with internal banking systems, APIs, databases, and enterprise data sources in a secure and scalable way Build and maintain guardrails, output validation, and safety layers so agent behavior stays accurate, compliant, and aligned with regulatory and data privacy requirements Apply prompt engineering, context engineering, and fine-tuning techniques (e.g. LoRA) to optimize model performance for banking-domain use cases Collaborate closely with cross-functional teams, particularly IT, security, and business units, to define, prioritize, and deliver AI projects Keep up with the latest developments in LLMs, agent frameworks, and AI tooling, and evaluate new technologies for adoption We are waiting for your application if you have: A bachelor's degree from a reputable university, preferably in Computer Engineering, Software Engineering, Mathematics, or a related field Minimum 3–4 years of experience in AI engineering or related roles, including hands-on experience building LLM-based applications Strong proficiency in Python and solid software engineering practices Hands-on experience with LLM APIs and agent frameworks or orchestration tools (e.g. LangChain, LangGraph, LlamaIndex, Langflow, or similar) Experience with RAG architectures, vector databases, and embedding models Familiarity with REST APIs and microservices Good command of SQL and experience working with enterprise data sources Experience with model serving, inference optimization, or self-hosted open-source models is a plus Experience with speech technologies (STT/TTS) or voice agents is a plus Awareness of AI safety, security, and data privacy principles in regulated environments Fluency in English, both written and spoken An open mind, a strong analytical point of view, problem-solving skills, and a passion for building things that people actually use If you’ve built agents before and want to work on them at the scale of a large bank, we’d like to hear from you.