Bashir Mohammed, PhD.

Amazon Web Services

AWS GenAI Startups Team

Amazon Web Services.
San Francisco,
CA 94111, USA.
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About me

I’m a hands-on Senior Lead GenAI Solutions Architect at AWS Startups, where I collaborate with some of the world’s most innovative founders to turn bold, early-stage ideas into scalable, production-ready GenAI and agentic systems on AWS. My mission is to help startups move faster, build smarter, and scale confidently by architecting intelligent, high-performance cloud infrastructures tailored to their unique goals.

I hold a Ph.D. in Computer Science and bring a wealth of research experience from my previous role at Lawrence Berkeley National Laboratory , where I specialized in developing AI and ML applications for intelligent networks, automatic control systems, quantum communication networks, and data provenance in high-performance computing and distributed systems.

At AWS, I lead technical design and implementation across the full GenAI lifecycle — from model selection and fine-tuning to multi-agent orchestration, cost optimization, and deployment. Whether it’s building LLM-powered copilots, vision-language pipelines, or autonomous reasoning agents, I focus on enabling startups to unlock real-world value through speed, reliability, and cost-efficiency.

Before joining AWS, I served as a Senior Staff Lead AI Architect at Intel, where I led pioneering efforts in AI at the Edge — designing and deploying LLMs, LVMs, and Multi-agentic systems that transformed industrial automation, IoT analytics, and enterprise operations. That work taught me one thing: the most transformative AI happens when cutting-edge research meets scalable engineering.

Today, I bring that same spirit to the startup ecosystem — empowering builders to experiment, iterate, and deploy with confidence on AWS,(Earth's most customer-centric company).

“Great AI isn’t just built — it’s engineered through collaboration, curiosity, and relentless iteration.”

Before then I was part of the Quantum Application Network Testbed for Novel Entanglement Technology(QUANT-NET) and Berkeley Lab's Cybersecurity R&D for Science and Energy in the Integrated Data Frameworks(IDF) group under the Scientific Data Division.

In the past I was a post-doctoral research scholar in the Computational Research Division(CRD) working on the Deep Learning and Artificial Intelligence High-Performance Networks(DAPHNE) project under the supervision of Mariam Kiran of Energy Sciences Network(Esnet) and John Wu at Berkeley Lab. I received my (MSc) in Control Systems Engineering under the supervision of Professor Peter J Fleming from the University of Sheffield, and a PhD in Computer Science from the University of Bradford in the United Kingdom under the supervision of Professor Irfan Awan and Professor Hassan Ugail. My work focused on developing AI and Machine Learning algorithms to control high-speed networks such as to avoid network traffic congestion, degraded network performance and network downtime for important Science Bigfile Transfers and experiments. My method led to minimizing network downtime, saving US department of Energy (DOE) costs, and freeing engineers to work on science experiments. Before then, during my Ph.D., my research contributed to the core network research and operations with my strong background in HPC and cloud, where I developed and built these infrastructures during my PhD (Bradstack).

I am a 2019 Berkeley Lab Research SLAM award winner. In 2019 I was among six postdoctoral research fellows from US National Labs invited to Washington DC to speak about their research to the Capitol Hill audience.

Current interest

  • Large Language Models (LLMs), Large Vision Models(LVMs), Small Language Models(SLMs) and Multi-Agentic Workflows

  • Real-time Machine Learning Control and Reinforcement Learning in High Speed Networks

  • Quantum Communication Networks

  • Software‐Defined Networks(SDN)

  • Cybersecurity R&D for Science and HPC workflows

  • Developing Machine Learning algorithm for large-scale distributed networks

  • Failure Prediction and Performance related issues on Cloud and HPC systems

  • Cloud Platform implementation (Chameleon Cloud, GENI , Openstack, AWS, Microsoft Azure, Google Cloud Platform)

  • Automatic Control systems

Honors and Awards

Professional Activities

Teaching

2021
University of California, Davis, California USA.

ECS 289I - 001 Winter 2021- Application of Machine Learning in Computer Networks

2020
Berkeley Lab Teaching Scholar Program , Berkeley, California USA.

2020 Berkeley Lab Teaching Scholar Candidates

2018
Leeds Trinity University, UK

Module leader for the Higher Education Funding Council of England (HEFCE) course on introduction to programming.

Contributing to the academic development, course management, teaching and research development.

Teaching and mentoring Second year students.

Teaching Introduction to Programming, Human Computer Interaction (HCI).

Introduction to Coding using Python and Java.

Introduction to JavaScript, Change Management and Version Control using tools such as Visual Studio, Bracket, Code Academy, Code Pen, GitHub, and Glitch.

2017
University of Bradford, UK

2016-7 ACYR - Cisco Routing and Switching Labs, Computer Communication and Networks (CCN) For 2nd Year Undergraduate Students.

2016
University of Bradford, UK

COS5010-B- Computer Communication and Networks (CCN) For 2nd Year Undergraduate Students.

2016
University of Bradford, UK

CM-1066D- Networks and Protocols For MSc Students in Network and Cyber Security

2015
University of Bradford, UK

CM-0228L - Software Engineering with Group Projects (SEGP)- For 2nd Year Undergraduate Students.

Professional Training and Certification

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