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Lead AI engineering and consulting engagements for enterprise clients in the UAE. Design and deploy agentic AI systems, LLM agents, RAG pipelines, and machine learning platforms on AWS and Azure. Manage and mentor a team of 5–10 technical specialists, advise senior stakeholders, define delivery roadmaps, and enforce secure, compliant MLOps practices for banking-grade production environments.

Develop scalable, secure, AI-powered banking applications using microservices, REST APIs, GenAI, RAG, agentic workflows, data pipelines, Docker, cloud platforms, and CI/CD automation. The role also involves architecture design, cloud-native deployment, cross-functional collaboration, stakeholder communication, and translating complex technical requirements into business solutions.

Lead complex AI, data science, and mobile banking projects from inception through deployment. Manage cross-functional teams, budgets exceeding $1M, Agile and traditional methodologies, project risks, governance, stakeholder alignment, Scrum ceremonies, documentation, and executive reporting. The role requires strong understanding of the AI/ML lifecycle, MLOps, responsible AI, data privacy, security, and regulatory compliance within financial services.

Develop statistical risk models and quantitative solutions for corporate lending, wholesale banking, treasury, liquidity, and regulatory capital. Build PD, LGD, EAD, RAROC, and RAC frameworks; apply Basel requirements and stress testing standards; write production-quality Python; and communicate model methodologies to banking clients and stakeholders. Preferred experience includes causal analysis, sequence models, large-scale distributed computing, and executive-facing dashboards.

Leads end-to-end delivery and release governance for AWS-based MLOps platforms, machine learning models, and cloud infrastructure. Translates data science requirements into technical plans, coordinates cloud and network dependencies, manages CAB approvals and production releases, and ensures rollback readiness. Oversees DevSecOps, model risk, cybersecurity, data privacy, production telemetry, model drift monitoring, and FinOps optimization in regulated enterprise environments.

Lead enterprise AI and machine learning architecture, delivery, and consulting engagements across the UAE. Build production-grade ML, deep learning, RAG, LLM agent, and MLOps systems on AWS and Azure. Advise senior client stakeholders, mentor AI and quantitative teams, resolve technical and organizational friction, and establish secure, compliant AI platforms using modern cloud, deployment, and governance practices.

The Business Analyst translates banking business needs into technical requirements for AI solutions. Responsibilities include facilitating requirements workshops, partnering with data scientists and ML engineers, validating AI use cases, defining model testing and bias detection strategies, supporting UAT and change management, documenting audit trails and decision logic, identifying automation opportunities, and training stakeholders on AI outputs and limitations.

Design and implement scalable data models, semantic layers, ontologies, and knowledge graphs within Lakehouse architectures. The role covers dimensional modeling, historical tracking, metadata standards, lineage, schema evolution, partitioning, and performance optimization. The Data Modeler collaborates with architects, engineers, quality teams, and senior stakeholders to support data governance, self-service analytics, interoperability, and enterprise data modernization.

Own project controls, portfolio governance, delivery health analysis, RAID tracking, executive dashboards, KPI reporting, stage-gate compliance, and audit-ready documentation across enterprise AI and data projects. The role also improves PMO standards, reporting workflows, and project management practices while collaborating with project managers, technical leads, and senior stakeholders.

Leads enterprise data architecture for fixed-scope AI consultancy projects. Designs and governs Databricks Lakehouse platforms across AWS and Azure, advises executive clients, manages architecture roadmaps and technical risk, mentors cross-functional engineering and data teams, and establishes secure, compliant data foundations. The role requires strong consulting, stakeholder management, cloud, distributed processing, MLOps, and regulated financial-services experience.