Manager, AI Engineering

Objective: Own the end-to-end AI engineering strategy, architecture, and delivery of secure, compliant LLM-powered and data-science platforms, conversational assistants, RAG pipelines, and ML-Ops that streamline DoF core processes, enhance fiscal transparency, and drive measurable operational efficiency, positioning DoF as the region’s benchmark for AI-driven public finance within three years.

Essential Duties:

• Lead the design, development, and deployment of AI solutions for DoF.
• Architect end-to-end GenAI and retrieval-augmented generation (RAG) pipelines leveraging both open and closed source ecosystems, vector databases, and feature stores.
• Design, build, and deploy advanced analytics solutions that leverage traditional machine learning techniques such as regression analysis, decision trees, clustering, and support vector machines to derive actionable insights from data, optimize processes, and enhance decision-making capabilities. Own full-stack implementation of Python, Node and TypeScript-based microservices, REST APIs, and cloud-native as well as On-Prem infrastructure.
• Implement ML-Ops best practices. CI/CD, model versioning, bias & drift monitoring. To ensure reliable, compliant delivery.
• Conduct prompt engineering, fine-tuning, and evaluation to maximize LLM accuracy, safety, and business value.
• Plan and execute A/B and Bayesian experiments; analyze results to optimize conversational flows and KPIs.
• Embed privacy, fairness, and regulatory controls in AI solutions in partnership with DoF’s AI governance framework.
• Lead and mentor cross-functional vendor squads; set technical roadmaps and manage Agile delivery.
• Establish coding standards, code reviews, and documentation for maintainable, high-quality software.
• Manage vendor and cloud relationships; evaluate emerging GenAI technologies and recommend adoption.
• Present technical strategy, roadmaps, and progress to executive stakeholders, building consensus and securing funding.
• Champion a culture of continuous improvement, knowledge sharing, and innovation within the AI role.
• Provide the necessary support to the direct manager, and perform any other related tasks as requested.

Qualifications:

• Bachelor's degree or higher in Artificial Intelligence, Machine Learning, or a similar field relevant to the job duties.
• A specialized certificate in the field of the job is preferred.

Experience:

• Preferable to have 10 to 15 years of experience in a similar field/position.
• Preferable at least 4 years’ experience in data-science, Artificial Intelligence, with knowledge in shipping large-scale chat or recent generative AI workflows focused in the financial sector.
• Proven track record in delivering conversational platforms within the financial domain.
• Full-stack engineering foundations: Python & TypeScript, REST APIs, cloud-native architectures (Azure exposure preferred), ML-Ops CI/CD pipelines
• Deep hands-on experience with Large Language Models (LLMs) and Generative AI (GenAI), including prompt orchestration, fine-tuning, RAG pipelines, vector databases, and feature stores.
• Production-level experience with conversational frameworks such as Azure AI Bot Service, Lex/Bedrock or similar, etc., along with orchestration of multi-journey assistants.
• Designs and runs A/B and Bayesian experiments, driving data-driven optimization of conversational flows.
• Proven leadership of cross-functional teams (approximately 10–15 engineers/scientists), with the ability to set technical roadmaps and mentor talent.
• Skilled in executive communication, building consensus, presenting business cases, and managing AI risks, bias, privacy, and regulatory compliance.
• Patents or publications in AI, along with prior experience in Middle East Banking or FinTech, are considered a strong plus.