AI Engineer

We are looking for an experienced and passionate AI Engineer to join our digital banking team and play a pivotal role in shaping the future of finance through cutting-edge Generative AI technologies. In this role, you will be a key contributor in developing intelligent, scalable, and secure AI solutions that transform how customers interact with their bank-delivering smarter, more personalized, and intuitive digital experiences.

Your Day-to-Day:

• Design and build AI-driven solutions that address complex, free-text customer queries and enhance real-time personalized banking interactions.
• Develop Generative AI systems, including Retrieval-Augmented Generation (RAG) pipelines, AI agent frameworks, and other advanced GenAI solutions.
• Collaborate closely with product managers, data scientists, and engineers in a highly dynamic and innovative environment.
• Leverage a modern AI tech stack, including: Closed and open source language models, LangChain, LangGraph, LlamaIndex, Vector databases, AI orchestration platforms
• Ensure solutions are context-aware, secure, and scalable.
  
  

Requirements:

• Hold an M.Sc./Ph.D. degree in Computer Science, Engineering, Mathematics, Statistics, or another related quantitative field that offers a solid foundation for data science and machine learning - a must.
• +3 years of proven production experience in developing generative AI systems, including RAG pipelines, AI agent frameworks, and other advanced GenAI solutions - a must.
• Proficiency in Python and LLMs- a must.
• Experience with building agentic workflows with GenAI libraries and tools, such as LangChain, LangGraph, LlamaIndex, and Multi-Agent systems.
• Experience developing NLP models at scale, from inception to production, with business impact.
• Skill set includes software engineering, data infrastructure, and familiarity with RDBMS/NoSQL.
• Strong expertise in prompt engineering and prompt optimization techniques.
• Exhibit robust problem-solving skills, with the capability to identify business challenges, formulate data-driven hypotheses, and design experiments to test and validate these hypotheses.
  
  

Advantages:

• Experience in both deep learning and classical machine learning models.
• Strong background in NLP.
• Expertise in machine learning engineering, including model deployment and scalability.
• Research experience with a publication record is an advantage.