AI Engineer with experience building LLM pipelines, computer vision models, NLP applications, and data systems. My work sits across applied AI, research, and production engineering, with particular interest in GenAI, explainability, and quantitative finance.

• Vision-Language Models for deepfake detection
• Explainable AI and model governance
• Computer vision and segmentation
• NLP assistants and LLM pipelines
• Graph Neural Networks for social network and planning problems

• Python and SQL ETL pipelines
• Cloud deployment on AWS, Azure, and Oracle Cloud
• API development with FastAPI and Flask
• Model serving and MLOps workflows
• Real-time streaming with Kafka

Worked on AI-powered deepfake detection and explainability projects, including fine-tuned VLM pipelines, GRPO optimisation, deterministic XAI frameworks, and real-time deployment on Azure Kubernetes.

Built Python and SQL ETL pipelines, migrated on-premises data to AWS RDS, and supported automated reporting and Power BI dashboards for business teams.

Developed a fraud detection solution using machine learning, Kafka streaming, and AWS-based architecture for financial services use cases.

Built GNN-based classroom analytics and an NLP assistant that converted user requirements into scheduling constraints for classroom allocation.

Languages & Backend
Python, Java, R, SQL, FastAPI, Flask

Machine Learning
PyTorch, TensorFlow, Keras, Scikit-learn, XGBoost, OR-Tools

LLM / GenAI
Hugging Face, Qwen, Llama, OpenAI API, RLHF, XAI, ms-swift

Cloud / MLOps / Data
AWS, Azure, Oracle Cloud, Docker, Kubernetes, Kafka, MLflow, Git CI/CD, Linux