About Mohamed Sheriff
From Freetown to the world, and back again.
A pan-African technologist whose career runs from telecom software to submarine cables to the AI and data systems that now run on them.
Professional foundation
One career, told in order: the build years, the infrastructure years, and what I am building now.
I’m Mohamed Sheriff. Friends call me Moe. I’m an American with Sierra Leonean roots, and I think of myself as a global citizen. My work has taken me across Africa, Europe, the Middle East, India, Asia and the United States.
I started in telecom software. As a Senior Systems Designer at DST Innovis, which Amdocs acquired, I built business-intelligence systems inside the software telecom operators use to bill and serve their customers. At Accenture I worked as a business intelligence consultant for major United States corporate and government clients. The biggest was the 2010 U.S. Census: our team, working with Northrop Grumman, helped build the application that processed real-time data from census field workers, managed field operations and produced the reporting.
Then I went home. As IT Manager at the Sierra Leone Telecommunications Company, and later as CEO and Managing Director of Sierra Leone Cable Limited (SALCAB), I worked on the infrastructure that connects a country to the internet, including Sierra Leone’s first terrestrial fiber network. On the Africa Coast to Europe (ACE) submarine cable, I co-chaired the Technical Working Group while the cable was being built, then chaired its Assignment, Routing & Restoration Subcommittee for more than five years, coordinating capacity and outage restoration across a system serving 24 countries. When a cable breaks, millions of people go offline. That job taught me what “critical” really means.
After SALCAB, I joined the FutureCom project team as the consultant in charge of data for Sierra Leone’s National Social Security and Insurance Trust (NASSIT), the statutory trust that runs the national pension scheme. I led the cleansing and migration of NASSIT’s data into production: more than 50 million records in total, spanning member, contribution, benefit and biometric data that workers and their families depend on.
Today I run Anunnaki Technology Solutions, where I design AI and data systems for organizations that need them to work in the real world. I host Africa Future Nexus, a YouTube channel explaining the technology shaping Africa and its diaspora. And for fun, and as a lab for AI production, I make music as MoeFunk.
How the foundation was built
Five stages, each of which changed how I approach the current work.
None of this is presented as a former career. Each stage is the reason a particular part of the AI work is done the way it is.
Software and databases
Learning what production actually means
Early work in software, databases, reporting and business intelligence established how applications and organizational data hold up under daily operational load — and how they fail.
Enterprise consulting
Reading systems built by other people
Oracle E-Business Suite, ETL, analytics and cross-module reporting with Accenture, Amdocs, BMC Software, DST Innovis and Hanover Compressor. Tracing transaction flows through enterprise data models is the same skill an AI opportunity assessment needs.
Telecommunications infrastructure
Building things that cannot be allowed to stop
Submarine cable construction, assignment routing and restoration, technical procurement, terrestrial fiber deployment and DWDM educational connectivity. Infrastructure work is where the difference between a working demonstration and a running service becomes obvious.
Executive leadership
Owning the consequences
Chief Executive Officer and Managing Director of Sierra Leone Cable Limited, with responsibility for governance, stakeholders, procurement, operational risk and service continuity. Technology decisions stop being technical when the organization answers for them publicly.
Founder and AI practice
Applying all of it
Through Anunnaki Technology Solutions LLC: AI strategy, automation, SaaS product planning, data, media systems, education and community platforms — built with the same discipline the infrastructure work demanded.
Working principles
A disciplined approach to technology and AI.
- 01
Begin with the problem
Technology selection should follow a clear understanding of the current process, stakeholders, constraints, risks, and desired outcome.
- 02
Make decisions visible
Requirements, workflows, assumptions, priorities, acceptance criteria, and architectural choices should be documented clearly.
- 03
Treat data as infrastructure
Automation and AI depend on data quality, ownership, definitions, access, structure, and responsible use.
- 04
Design for adoption
A technically capable system still needs understandable workflows, responsible ownership, training, support, and stakeholder participation.
Leadership perspective
What running critical infrastructure teaches you about AI projects.
Infrastructure has no tolerance for a system that works in a demonstration. It has to survive a bad day, an absent expert, a vendor dispute and a budget cycle. Executive responsibility for that means budgets, policies, people, risk, institutional priorities and public expectations were never someone else’s problem.
Applied to AI work, it produces a specific bias: name who will own the thing once it exists, be explicit about what it should not be trusted to do, and prefer the smaller system that can actually be operated over the larger one that cannot.
It also makes the conversation work in both directions — with the engineers who will build it and with the board that has to approve it.
Anunnaki Technology Solutions LLC
A company created to explore and build practical solutions.
Founded in 2023, Anunnaki Technology Solutions LLC provides the organizational foundation for Mohamed Sheriff’s consulting, research, product-planning, AI, automation, media, education, and community-technology initiatives.
AI and workflow automation
Assessing opportunities and planning practical systems that reduce repetitive work or improve service delivery.
SaaS and product planning
Turning early ideas into product charters, MVP scopes, requirements, roadmaps, workflows, and acceptance criteria.
Data and decision support
Improving the organization, quality, reporting, and usefulness of operational information.
Education, media, and community platforms
Developing structured initiatives that support learning, communication, cultural connection, and digital participation.