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GenAI Engineer (m/f/d)
FTE
Riyadh, Saudi Arabia
20.11.2025
About the Role
Join our AI innovation team at a premier bank to continuously conducts a range of technology exploration, development, prototyping, and technology transfer work, relevant to the financial sector. This includes analysis, identification, and implementation of potential use-cases that leverage emerging technologies.
Key Responsibilities
Required Qualifications
Experience:
Technical Skills:
Join our AI innovation team at a premier bank to continuously conducts a range of technology exploration, development, prototyping, and technology transfer work, relevant to the financial sector. This includes analysis, identification, and implementation of potential use-cases that leverage emerging technologies.
Key Responsibilities
- Explore and implement generative AI applications for Suptech, including creating synthetic data environments to test AML/CTF controls without direct inspection, reducing supervision costs and improving coverage.
- Collaborate with regulatory stakeholders to guide and inform broader efforts to transform supervisory functions using AI-driven solutions.
- Work on design, development, and implementation of innovative projects and use cases that employ emerging technologies. In particular, data science and analytics, artificial intelligence, GenAI, and natural language processing in the financial sector in cooperation with the relevant internal/external stakeholders.
- Work with the project team, including business analysts, solution architects and developers to understand the solution requirements.
- Work with the project team on the development activities such as:
- Selecting features, building and optimizing classifiers using machine learning techniques.
- Data mining using state-of-the-art methods.
- Processing, cleansing, and verifying the integrity of data used for analysis.
- Creating automated anomaly detection systems and constant tracking of its performance.
- Work on delivery of innovation hub prototypes, MVPs, technical reports, identifying and delivering effective and successful outputs.
- Support the solution architect in the documentation of the low-level design and understand software requirements
- Code the solution as per the low-level design document and document the code as best practices and procedures
- Analyse information to recommend and plan the installation of new systems or modifications of existing systems
- Define and explain testing strategies, and perform prototype and MVPs testing in all phases of the development, to guarantee that the developed prototypes are performing in a flawless manner and as per the requirements.
- Design and generate AI-driven synthetic financial datasets that accurately replicate real-world transactional behaviors, enabling realistic compliance stress testing.
- Embed structured AML typologies, financial crime red flags, and sophisticated money laundering patterns into synthetic transaction flows.
- Apply adversarial machine learning (AML-ML) to stress-test existing compliance detection algorithms, identifying blind spots and weak signals.
- Establish quantitative benchmarks for AML detection performance, measuring false positive/negative rates, detection lag, and operational efficiency.
- Develop scoring mechanisms for compliance system resilience, testing institutions’ ability to detect and react to synthetic AML threats.
- Conduct controlled regulatory stress tests to assess how well compliance teams and automated systems handle high-risk financial crime scenarios
- Design and develop conversational AI systems and intelligent chatbots for enterprise banking applications
- Build and operationalize generative AI models aligned with business objectives and regulatory requirements
- Implement RAG systems and agentic workflows to enhance contextual relevance and task automation
- Develop and optimize prompt engineering strategies for maximum LLM performance
- Build robust, scalable ML pipelines and deploy AI solutions in production environments.
Required Qualifications
Experience:
- Minimum 5 years in AI/ML roles, specialized in GenAI solutions.
- Demonstrated experience in designing and developing conversational AI systems and intelligent chatbots for enterprise use cases.
- Strong proficiency in building and fine-tuning generative AI models aligned with specific business objectives.
- Experience in synthetic data generation for financial or compliance applications and familiarity with AML typologies and fraud detection frameworks is highly desirable.
- Exposure to Suptech or Regtech initiatives and understanding of supervisory technology trends is a plus.
Technical Skills:
- Hands-on expertise in implementing Retrieval-Augmented Generation (RAG) and agentic workflows to enhance contextual relevance and task automation.
- Skilled in prompt engineering, including iterative design and testing to optimize the performance of large language models (LLMs).
- Proven ability to build robust, scalable ML pipelines using platforms
- Solid understanding of Google Cloud Services and development environments.
- Experience deploying and operationalizing Azure Foundation Models and Azure OpenAI services within production environments.
- Familiarity with containerization and DevOps practices, including Docker and CI/CD pipelines, for efficient and scalable deployment.
- Advanced programming skills in Python, with a focus on developing high-quality, maintainable code for AI/ML applications.
- Knowledge of adversarial machine learning techniques, compliance stress-testing methodologies, and Suptech use cases is a strong advantage.