Mohamed Leila
My job is to help companies figure out where AI actually fits in their work, and where it doesn't. Most AI consultants either give you a strategy deck or build you a prototype. I do both, and stay involved long enough to make sure the system works in your business. A decade of building AI systems across very different industries has taught me one thing: the AI itself is rarely the hard part. The hard part is the data, the workflows, and the question being asked.
I came to AI through a longer road. Physics in Cairo, environmental monitoring in London, then a PhD on biofuel supply chain optimization in Montreal. The through-line across all of it has been mathematical modelling as a way to think through hard problems.
Where I've worked
2026 – Present
Founder
Adaptive Gradients
AI consulting for mid-size, operations-heavy companies. Help them figure out where AI fits, build the data and infrastructure to support it, and put working systems in place.
2024 – 2025
Co-founder & CTO
Grownomic AI
Built agentic workflows for Google Ads campaign management, an AI-assisted landing page builder used by paying clients, and custom ChatGPT applications. Owned architecture and engineering end-to-end. Started and shut down inside a year.
2022 – 2024
Manager, Responsible Innovation Platform
Axon
Led ML engineers building a responsible innovation platform for computer vision, LLMs, and automatic speech recognition. Built automated evaluation workflows for ethical AI performance across international markets. Designed privacy-preserving, GDPR-compliant evaluation pipelines.
2021 – 2022
Applied Research Scientist
ServiceNow Research
Worked in the AI Trust & Governance Lab on privacy-preserving ML and federated learning. Trained ServiceNow's first federated learning model. Developed adversarial attacks to evaluate the privacy of enterprise language models.
2019 – 2020
Specialist Data Scientist
Rio Tinto
Led data science projects for Canada's largest iron ore producer. Optimized smelting operations and mine feed scheduling with ML. Built Spark ETL pipelines on AWS.
2018 – 2019
Data Engineer
Wavo.me
Built the early cloud architecture of Wavo's Music Intelligence Platform. Developed ETL pipelines in Spark and AWS (S3, EMR, RDS, Glue, Athena) and time series forecasting systems to prioritize expensive API calls.
2017 – 2018
Operations Research Architect
Lean Systems
Designed vehicle-routing algorithms for ground transportation and maintenance scheduling models for business aviation. Built interactive web apps for fleet operations visualization. Contributed to the core optimization engine.
Academic background
PhD, Renewable Resources
McGill University
Applied optimization and geospatial modeling for biofuel supply chain viability.
MSc, Environmental Monitoring & Management
King's College London
BSc, Physics
The American University in Cairo
Want to work together?
If you're building with AI and want a technical partner who's done it before, I'd like to hear what you're working on.
Read detailed project writeups on my personal site.
Get in touch