Remote: Yes

Willing to relocate: Yes, Open to discuss

Technologies: Python, Pandas, Flask, JavaScript, SQL/NoSQL, PostgreSQL, MySQL, MongoDB, AWS, Docker, Kubernetes/kubectl, Airflow, Celery, OpenAI, Hugging Face Transformers, LangChain, RAG, LoRA fine-tuning, prompt engineering, LLM evaluation, CDISC, clinical-trial workflows

Résumé/CV: https://amrs-tech.github.io/assets/Ahamed-Musthafa-RS-Resume...

Email: amrs.tech@gmail.com

Senior Generative AI Engineer with 7 years of software engineering experience, including 3+ years building production LLM systems for clinical and life-sciences teams. I build GenAI copilots, RAG pipelines, fine-tuned models, prompt systems, and backend services that make AI usable inside real product constraints.

Recent work: - Built a production GenAI copilot for Interactive Review Listing / clinical data-review workflows, reducing query-build time by 75%. - Fine-tuned open-source LLMs with LoRA on synthesized datasets for logical-discrepancy classification, reaching 80%+ accuracy. - Designed model-service interfaces and configurable rule engines that reduced integration time by ~50% and improved platform efficiency by up to 80%. - Built no-PHI/no-PII clinical AI tooling for public adverse-event exploration with source-grounded retrieval over openFDA, FDA labels, RxNorm, and PubMed.

Previously worked as a backend developer building Python/Django/PostgreSQL pharma CRM and patient-support systems. I’m strongest at productionizing LLM workflows: natural-language interfaces over complex backend systems, clinical/pharma data workflows, RAG, fine-tuning, evaluation, and reliable Python/AWS backend integration.

Portfolio: https://amrs-tech.github.io