Full-Stack AI Engineer

WE’RE HIRING AT robotat 🦾 Full-Stack AI Engineer | Full-Time robotat is looking for a technically strong Full-Stack AI Engineer with proven banking or financial services experience to develop secure, scalable, and production-ready AI solutions. The ideal candidate combines full-stack development expertise with practical AI engineering skills and an understanding of the security, governance, and reliability requirements. Key Responsibilities • Develop end-to-end AI applications, from backend services and APIs to user-facing interfaces. • Build and integrate AI/ML pipelines using large language models (LLMs), retrieval-augmented generation (RAG), NLP, OCR, speech processing, and predictive modelling. • Integrate applications with enterprise APIs, databases, and authorized document repositories. • Evaluate AI performance, improve output reliability, and implement testing, monitoring, and continuous improvement practices. • Implement appropriate security controls, access management, audit logging, explainability, and human-in-the-loop review. • Deploy and maintain AI applications in on-premises or restricted environments, supporting data privacy and infrastructure requirements. • Work with business and technical stakeholders to translate banking requirements into practical, production-ready solutions. Requirements • Proven professional experience in banking or financial services, with a clear understanding of relevant business workflows and the handling of sensitive financial data. • Strong programming skills in developing backend services and APIs. • Practical full-stack development experience, including modern frontend technologies such as React and TypeScript, or equivalent. • Hands-on experience building and integrating AI/ML solutions, including LLM-based applications and RAG. • Familiarity with data processing, model evaluation, automated testing, version control, and production deployment practices. • Experience with Docker, CI/CD, and MLOps or equivalent deployment and operational workflows. • Understanding of secure software development, data governance, and the importance of traceable and reliable AI outputs. • Strong analytical, problem-solving, communication, and collaboration skills. Preferred Qualifications • Experience in one or more banking-focused areas, such as credit-risk modelling, AML/CFT, fraud detection, transaction monitoring, or banking quality assurance. • Familiarity with core banking integrations, RegTech, model-risk management, or AI deployment in highly regulated environments. If you’re passionate about building intelligent applications, solving complex technical challenges, and turning AI innovations into real-world solutions, we want to hear from you. 📍 Location: Burj Jasim, Kuwait City 📧 Apply: [email protected] Apply now or share this opportunity with someone who may be a great fit. Show more Show less

Proven professional experience in banking or financial services, with a clear understanding of relevant business workflows and the handling of sensitive financial data. Strong programming skills in developing backend services and APIs. Practical full-stack development experience, including modern frontend technologies such as React and TypeScript, or equivalent. Hands-on experience building and integrating AI/ML solutions, including LLM-based applications and RAG. Familiarity with data processing, model evaluation, automated testing, version control, and production deployment practices. Experience with Docker, CI/CD, and MLOps or equivalent deployment and operational workflows. Understanding of secure software development, data governance, and the importance of traceable and reliable AI outputs. Strong analytical, problem-solving, communication, and collaboration skills.

Develop end-to-end AI applications, from backend services and APIs to user-facing interfaces. Build and integrate AI/ML pipelines using large language models (LLMs), retrieval-augmented generation (RAG), NLP, OCR, speech processing, and predictive modelling. Integrate applications with enterprise APIs, databases, and authorized document repositories. Evaluate AI performance, improve output reliability, and implement testing, monitoring, and continuous improvement practices. Implement appropriate security controls, access management, audit logging, explainability, and human-in-the-loop review. Deploy and maintain AI applications in on-premises or restricted environments, supporting data privacy and infrastructure requirements. Work with business and technical stakeholders to translate banking requirements into practical, production-ready solutions.