Tahir Askarli

Lead the development of sophisticated RL environments for UAV swarm coordination and autonomous decision-making, significantly enhancing operational capabilities for mission-critical systems.

Design and implement deep learning perception systems for advanced UAV platforms (TB2, TB3), optimizing real-time performance and reliability.

Optimize edge inference using TensorRT and ONNX for real-time deployment, boosting system efficiency and responsiveness.

Build mission-critical Computer Vision systems, including autonomous landing on naval platforms, achieving 99%+ reliability in high-stakes operational environments.

Ensure stringent compliance with production-grade system architecture and comprehensive documentation, maintaining high standards for reliability, scalability, and maintainability.

Developed advanced AI systems for oil & gas signal processing in the ACG field, optimizing data analysis and contributing to operational efficiency.

Built and deployed anomaly detection models for real-time sand event detection, significantly reducing false positives and preventing critical system failures.

Delivered a Python-based visualization tool in collaboration with BP engineers for pressure transient analysis, providing critical insights and supporting decision-making.

Built and deployed production-grade LLM, RAG, and Vision-Language systems, enhancing retail AI capabilities and driving improved data processing efficiency.

Designed and implemented complex multimodal pipelines, effectively combining vision, language, and structured data to deliver comprehensive AI solutions.

Developed and operationalized robust evaluation and risk assessment frameworks for production AI, ensuring system reliability and ethical deployment in high-stakes contexts.