Yasir Ali
ML EngineerTechnical Cofounder, Yarn AI
Yarn.ai
AI is analyzing your overall score…
Identifying your key strengths…
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Assessing your cultural and operational fit
Technical Cofounder, Yarn AI
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer and experienced Tech Lead, with a language and tech agnostic mindset, and always looking to learn new skills to deliver creative engineering solutions for problems. Like to work in a stimulating and progressive environment with challenges that demand creativity and constant learning
Darmstadt University of Applied Sciences
Master of Science (MS), Computer Science
January 1, 2013 – January 1, 2014
Lahore University of Management Sciences
Master of Science (MS), Computer Science
January 1, 2012 – January 1, 2015
National University of Computer and Emerging Sciences
Bachelor of Science (BS), Telecommunications Engineering
January 1, 2003 – January 1, 2007
Yarn.ai
Technical Cofounder
November 1, 2025 – Present
Carbon13
Venture Builder
August 1, 2025 – Present
Part-time
Technical Consultant
March 1, 2025 – July 1, 2025
Delivery Hero
Engineering Manager - Logistics Optimisation
July 1, 2022 – August 1, 2024
Berlin, Germany
connectavo
Head of Engineering / Lead AI
August 1, 2020 – March 1, 2022
Berlin, Germany
connectavo
Principal Engineer / Lead Artificial Intelligence
April 1, 2016 – August 1, 2020
Berlin, Germany
QBXNet Ltd.
Lead Artificial Intelligence
May 1, 2015 – April 1, 2016
Nokia Siemens Networks
Global Technical Support Engineer
June 1, 2011 – May 1, 2012
Pakistan
Nokia Siemens Networks
Technical Support Engineer
April 1, 2010 – June 1, 2011
Pakistan
Motorola
RAN Support Engineer
June 1, 2009 – April 1, 2010
Motorola
System Integration Engineer
June 1, 2008 – June 1, 2009
Master's Research Thesis: Detection and Classification of Complex Motor Action from EEG for Brain Computer Interfaces
September 1, 2014 – Present
The study researches advanced Power Spectrum Analysis techniques and feature extraction for Machine Learning Algorithms for Brain Computer Interfaces. The study has successfully attempted to show that Time Frequency Analysis of EEG activity over customized collection montages, fed to a suitable Classifier can detect between classes of Complex Action tasks of the arms.
Research Project: Statistical Non Rule-Based Grammar Checking
September 1, 2014 – Present
A Natural Language research project that proposes and implements methodology into Statistical Grammar Checking without rules. The proposed idea was implemented and a 75% accuracy was achieved in Error Detection, while a 53% accuracy was seen in Error Correction. Considering that purely statistical Error Correction was not the part of the original task or the proposal, the results were quite encouraging and warrant further investigation towards statistical as well as hybrid systems.
Research Project: A Feedback Approach to Task Partitioning in Heterogeneous Architectures
March 1, 2013 – Present
A study into parallel execution of serial program code on GPGPUs with a task partitioning strategy for optimal performance
MIT Deep Learning Bootcamp
Massachusetts Institute of Technology
June 24, 2026 – Present
Multi AI Agent Systems with CrewAI
DeepLearning.AI
June 24, 2026 – Present
Machine Learning in Production
DeepLearning.AI
June 24, 2026 – Present
Stanford Machine Learning Specialization
Cousera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project history and recent roles (Technical Cofounder at Yarn.ai, Venture Builder at Carbon13) show an inclination towards innovation, sustainability, and entrepreneurial ventures. Their diverse experience across telecommunications, industrial IoT, and AI, coupled with a strong academic research background, suggests adaptability and a continuous learning mindset. However, the recent shift towards co-founder/venture builder roles might indicate a preference for early-stage, high-autonomy environments, which may need to be explored for alignment with a more structured ML Engineer role.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership, team-building, and mentoring skills from their Engineering Manager and Head of Engineering roles. They have experience in setting up people-centric team cultures, introducing core processes, and managing critical services, indicating a good operational fit for senior roles. Their involvement in incident management and stakeholder engagement also highlights strong problem-solving and communication abilities.