Tahir Awan

Led end-to-end development of enterprise-grade LLM applications, including data pipelines, SFT/RLHF fine-tuning, and deployment. Built scalable RAG pipelines and evaluation frameworks, improving model accuracy by 35%+ for multiple enterprise use cases.

Architected AI-driven smart city systems including predictive analytics, infrastructure optimization, and predictive maintenance models. Built scalable ETL/ML pipelines for real-time decision-making and developed stakeholder data visualization dashboards.

Delivered end-to-end AI solutions for global clients across healthcare, fintech, e-commerce, education, and enterprise systems. Built chatbots, NLP pipelines, automation tools, recommendation engines, and ML workflows from scoping through deployment, monitoring, and optimization.

Developed computer vision pipelines for image recognition, classification, and content recommendation. Built data labeling and augmentation pipelines and integrated models into production via scalable APIs and monitoring tools.

Developed a full-stack AI platform for audio and sensor analytics using Python, ML, and computer vision. Built real-time streaming ingestion pipelines and integrated LLMs/ML models for predictive insights, anomaly detection, and audio content analysis, supported by AWS/GCP infrastructure and CI/CD deployment automation.

Designed and delivered end-to-end AI automation systems, reducing client workflow bottlenecks by 50%. Built NLP pipelines for document processing, semantic search, and summarization, and integrated AI into SaaS products while optimizing performance for high-volume processing.

Built an AI-powered SaaS platform for enterprise clients integrating NLP, LLMs, and automation tools. Designed scalable backend APIs and data pipelines, implemented summarization/recommendation/predictive analytics features, and optimized production deployments with CI/CD.

Built AI-driven healthcare analytics and predictive models using large-scale medical datasets and real-time monitoring systems. Implemented diagnostic recommendation engines that improved clinical decision support accuracy by 30% and delivered end-to-end ML pipelines to deployment and monitoring.

Led AI-first e-learning and e-commerce platform development with ownership of technology strategy. Designed personalization algorithms, recommendation engines, and predictive models, scaling to 10,000+ daily active users while improving load speed by 50% and engagement by 40%.

Built an AI auditing suite to analyze model bias, fairness, and compliance across enterprise AI systems. Developed explainability tools, performance monitoring and error tracking dashboards, and conducted end-to-end evaluation of LLMs and ML models.

Built backend systems integrating ML models for enterprise clients, including scalable APIs, data pipelines, and analytics dashboards. Optimized system performance and AI model efficiency for production use.

Built real-time data pipelines and monitoring tools for multiple client industries. Supported end-to-end AI solution delivery (including hotel management, accounting, and educational platforms) and improved system performance and model accuracy.

Earned a Bachelor’s degree in Computer Science from Punjab University College of Information Technology from 2009 to 2013.