Hamza Shaikh

Designed enterprise-grade LLM applications using AWS Bedrock, LangChain, and Transformers; reduced infrastructure costs by 50%. Led development of FinCrime AI agents serving 200+ analysts, improving workflow efficiency by 3x and response accuracy by 40%. Built production-grade FastAPI-based APIs with 99.9% uptime, integrating prompt versioning, monitoring, and automated evaluation pipelines for LLM hallucination tracking; implemented serverless architectures with AWS Lambda, S3, and EC2 to serve domain-specific models; achieved 30% token reduction via prompt engineering and data optimizations. Implemented custom prompt engineering and fine-tuned LLMs on 10k+ domain-specific examples; increased model accuracy from 65% to 75% for financial compliance tasks.

Developed a sentiment-based chat module using GenAI from inception for a platform with 10k+ monthly active users. Led end-to-end implementation including API design, deployment, and model optimization; performed model selection, fine-tuning, and prompt engineering across 15+ LLM variants; achieved 92% sentiment classification accuracy and reduced response time by 40%.

Developed ML-based route prediction system for chemistry research, helping scientists optimize molecular synthesis pathways; executed data analysis projects using Python, Power BI, and statistical methods for chemical manufacturing optimization; reduced chemical synthesis iterations by 3–4 cycles on average, decreased chemical waste by 15–20%, and saved annual material costs; built predictive models to minimize synthesis routes, reducing time and resource consumption by 25% across 100+ research projects.