Short
Abdullah Al Mamun, PhD is a distinguished AI researcher and industry expert in Recommender Systems (RecSys) and Generative AI (GenAI). He is currently a Sr. ML Engineer at Atlassian on the Central AI team, where he builds and improves SMART Answer generation for Jira/Confluence Search using RL-based fine-tuned LLMs and multi-Agent AI architecture.
Previously, he was a Member of Technical Staff at Aisera, architecting end-to-end multi-agentic AI systems for enterprise IT/HR — driving ~$3.4M estimated ARR, fine-tuning LLaMA-3 on AWS to save $4M in CAPEX vs. GPT-4, and enhancing the RAG pipeline for a ~93% real-time semantic-search improvement. Before that, he was a Machine Learning Engineer at Meta, fine-tuning LLaMA 3 for large-scale ad creation (13% CTR ↑, 6% CVR ↑, ~$497M iRev) and leading AutoCA audience clustering for personalized ads ranking (~$109M iRev).
Academically, he holds a PhD in Computer Science from Florida International University, focused on interpretable applied ML for early cancer detection and drug recommendation. His journey spans research, academia, and production-level AI systems—with deep expertise in LLMs, Transformers, RAG, RL fine-tuning, and scalable ML systems.
Long
Abdullah Al Mamun, PhD, has spent over a decade at the forefront of ML research and applied AI, with a mission to bridge academic excellence and industry-scale impact. His journey spans multiple countries, top-tier institutions, and FAANG, culminating in his current role at Atlassian shaping the future of enterprise agentic AI for Jira and Confluence Search.
He began his academic career in Bangladesh, earning a BS in CSE from Dhaka University of Engineering and Technology (DUET), where his early interest in machine learning and natural language processing (NLP) took root. Pursuing deeper expertise, he moved to KSA, earning a Master’s in Computer Engineering from King Fahd University of Petroleum & Minerals (KFUPM). During this period, he developed an LSTM-based sentiment analysis system that achieved 98% accuracy on customer feedback data.
In 2017, he joined Qatar University as a machine learning researcher and went on to win the 2nd GCC Robotics Challenge, a milestone that recognized his innovation in AI and robotics.
Later that year, Abdullah moved to the United States to pursue a PhD in CS at Florida International University (FIU). His research focused on interpretable deep learning for early-stage cancer detection and drug recommendation. His work resulted in multiple publications and travel fellowships to premier conferences such as ACM BCB and IEEE BIBM, reflecting both academic rigor and translational impact.
In 2022, Abdullah transitioned to industry, joining Meta (formerly Facebook) as a Machine Learning Engineer. At Meta, he worked on large-scale ads ranking, personalization, and generative ad creation using cutting-edge techniques like MTML and Transformer-based sequence learning models. He collaborated with Meta AI to integrate LLMs into ad-creation workflows — fine-tuning LLaMA 3 (SFT, RLHF, KV-Cache, 4D parallelism, quantization, distillation) and delivering 13% CTR ↑, 6% CVR ↑, and ~$497M in incremental revenue. He also led AutoCA audience clustering for ads ranking via targeting relaxation, contributing another 0.1% iRev improvement (~$109M).
In 2024, Abdullah joined Aisera as a Member of Technical Staff, where he led the design and deployment of multi-agentic AI systems for enterprise IT and HR automation — driving ~$3.4M estimated ARR. He architected a scalable, reusable onboarding agent and spearheaded the migration from commercial APIs (e.g., GPT-4) to in-house fine-tuned LLaMA-3 models on AWS, saving $4M in CAPEX. He also enhanced the RAG pipeline, improving real-time semantic search performance by ~93%.
Most recently, Abdullah joined Atlassian as a Sr. ML Engineer on the Central AI team, where he builds and improves SMART Answer generation for Jira and Confluence Search. His work centers on RL-based fine-tuned LLMs and multi-Agent AI architecture, driving the next generation of enterprise search experiences for millions of users across thousands of organizations.
Throughout his career, Abdullah has developed deep expertise in RecSys, LLMs, Transformers, RAG, vector databases, PyTorch, Hugging Face, LangChain, and inference optimization. He holds certifications from Google Cloud and the University of Illinois Urbana-Champaign, and continues to contribute actively to the field through open-source, research, and real-world deployment of AI systems.
Now based in Fremont, California, he remains focused on building AI that is not only intelligent—but scalable, reliable, and transformative for enterprises and society alike.
Outside of work, Abdullah leads an active lifestyle with a love for badminton, skiing, hiking, and mountain biking. He is also passionate about nature photography and has a deep appreciation for the outdoors. A frequent traveler, he has visited 16+ countries so far and continues to explore new cultures and landscapes to fuel his curiosity and creativity.