Secretary

• Led hands-on workshops covering LLM architectures, tokenization, embeddings, and transformer workflows for 100+ students.
• Designed and delivered technical training modules on prompt engineering, RAG systems, and agent frameworks like LangChain and CrewAI.
• Mentored members in building LLM-powered applications, including chatbots and agentic automation tools.

ML/AI Engineer – LLM Systems

• Worked on production ML systems with an emphasis on reliability, robustness, and user-facing performance.
• Designed and deployed ML inference services using FastAPI for low-latency, scalable model serving.
• Conducted systematic failure-mode and robustness testing, improving model reliability by 35–50%.
• Built feedback-driven pipelines incorporating user interaction data to continuously improve model behavior.

Machine Learning Engineer (Multimodal Visual AI)

• Designed and trained a data-centric multimodal generative vision system converting sketches into photorealistic images.
• Built end-to-end ML pipelines covering dataset curation, preprocessing, training, and interactive inference.
• Performed visual failure-mode analysis to identify geometry drift and annotation ambiguity.
• Optimized inference for low-latency interactive workflows, exposing models through user-facing interfaces.

Assistant Professor

• Designed and deployed automated data management systems using Python and SQL, reducing analytical turnaround time by 60%.
• Mentored 12+ student technical teams through hackathons and research expos.
• Built Python/SQL automation for grading and plagiarism detection, cutting manual academic workload by 60%.

ML Engineer (Medical Imaging)

• Built data preprocessing and feature-engineering pipelines for large-scale medical imaging datasets.
• Developed and evaluated CNN-based diagnostic models, performing detailed error analysis.
• Created data audit and validation tools to ensure dataset integrity and reproducibility.