Ahamed Moosa

• • Led the architecture and hands-on implementation of multiple production AI platforms across finance, pricing, sales, and customer experience, with a strong focus on Agentic AI systems capable of reasoning, deciding, and acting within enterprise workflows.

• • Designed scalable, cloud-native AI architecture on Azure (AKS, Azure OpenAI, AI Search, Blob, DevOps) to support autonomous LLM agents operating reliably in high-availability environments.

• • Built a residual value and used-car pricing platform with full MLOps, enabling downstream AI agents to consume pricing intelligence for automated decision support, contributing to ~25M AED business impact.

• • Architected LLM-powered agentic sales assistants with prompt orchestration, memory/context handling, tool calling, and fallback strategies, where agents securely interacted with CRM, inventory, and pricing systems via APIs to fetch data, update records, and guide sales conversations — driving a 20% uplift in vehicle sales.

• • Implemented advanced RAG pipelines with semantic search and vector retrieval to ground agent responses using internal knowledge bases, reducing hallucinations and improving decision quality.

• • Engineered a real-time call center sentiment and insight extraction pipeline using Speech-to-Text and LLM-based NLP, enabling AI agents to summarize calls, extract entities, and recommend next-best actions to CRM users.

• • Owned end-to-end MLOps and observability ensuring versioning, monitoring, drift detection, and continuous improvement for both ML models and LLM agents in production.

• • Developed backend microservices (Python, FastAPI, Node.js) that expose secure tools and actions for AI agents to interact with enterprise systems at low latency. Built executive Power BI dashboards to track how AI and agent-driven actions directly influence Residual Value, Pricing, and Sales KPIs.

• • Led architecture and hands-on development of enterprise AI products including Lift Manager Solution and Façade Inspection Solution deployed globally.

• • Defined reference architecture for AI product platforms ensuring reusability, scalability and maintainability across clients.

• • Designed and implemented scalable ML pipelines for predictive maintenance, anomaly detection and computer vision including data ingestion, preprocessing, feature engineering, model training, evaluation and deployment.

• • Built microservices-based ML serving architecture using Python, REST APIs and containerization enabling seamless integration with web and mobile applications.

• • Developed computer vision pipelines for façade inspection including image preprocessing, object detection, defect classification and result aggregation.

• • Implemented end-to-end MLOps framework including CI/CD pipelines, Docker containerization, Kubernetes orchestration, model versioning, performance monitoring and automated retraining workflows.

• • Owned production deployments and monitoring ensuring model stability, performance tracking, drift detection and continuous improvement.

• • Designed analytics dashboards and user interfaces to expose AI insights to operations and leadership teams.

• • Worked directly with global stakeholders, product managers and engineering teams to translate business problems into scalable AI system designs.

• • Solutions continue to generate multi-million-dollar annual revenue.

• • Built demand forecasting models for 3,000+ SKUs across 18 warehouses using advanced ML techniques.

• • Designed safety stock and space optimization models improving warehouse utilization and cost efficiency.

• • Productionized forecasting pipelines in collaboration with supply chain teams.