Fathima Thasleem
ML EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Head of Department | AI & Robotics Trainer | STEM | Abacus | Gen AI | Artificial intelligence | Tech Recruiter | Curriculum Development
Dynamic and extensively experienced professional specializing in AI & Robotics Training, curriculum development, and educational leadership. With a proven track record in the UAE's leading schools and institutions, adept at supervising and training trainers, developing KHDA-approved curricula, and spearheading innovative STEM, Abacus and Gen AI tools programs. Skilled as a Head of Department, delivering impactful learning experiences, fostering student engagement, and ensuring excellence in AI and Robotics education. I am committed to driving innovation and inspiring others within the technology and education sectors.
APJ Abdul Kalam Technological University
Master of Technology - MTech, VLSI Design and Signal Processing
September 1, 2020 – July 1, 2022
Annamalai University
Master's degree, MBA-Human Resource Management
June 1, 2019 – May 1, 2021
Visvesvaraya Technological University
Bachelor of Engineering - BE, Electronics and Communications Engineering
August 1, 2013 – June 1, 2017
Futurise Institute
Department Head (AI & Robotics)
December 1, 2023 – Present
Dubai, United Arab Emirates · On-site
Think-N-Innovate
Senior Robotics & AI Trainer
May 1, 2023 – December 1, 2023
Dubai, United Arab Emirates · On-site
Team Interval
Online Tutor
September 1, 2022 – December 1, 2022
Alfa Capital Management
Senior Technical Consultant
October 1, 2020 – Present
Dubai, United Arab Emirates · Hybrid
EC Academy
Administrator, Teacher
March 1, 2019 – July 1, 2020
Fortunerobotics
Robotics Trainer, Senior Faculty
February 1, 2019 – June 1, 2023
Hybrid
Sky Zone Aviation Academy
Aviation Communication Teacher
April 1, 2018 – February 1, 2019
TI Central School
Teacher
July 1, 2017 – March 1, 2018
Freelance
Abacus Trainer
January 1, 2011 – Present
Implementation of privacy preserving, secure Machine Learning and Deep Learning algorithms for epileptic seizure detection
November 1, 2021 – June 1, 2022
Epilepsy is often determined by examining the brain signals that brain neurons make. Monitoring is usually done using an electroencephalogram(EEG). These signals generate a lot of data which is used for analysing and detecting seizures and is a difficult task. Without compromising performance, machine learning is used to predict it. The principal difficulties here are classifiers and features. Deep learning computational methods combined with electroencephalogram have received much attention in recent years in the area of epileptic seizure detection. The patient’s confidentiality regarding his/her treatment plays a vital role and should be protected. Privacy applies to a person. For protecting their private data, we introduce Without transferring the local data samples, federated learning, a machine learning technique, trains an algorithm across several distributed edge devices or servers that store local data samples. I compared machine learning models with convolutional neural network (CNN) to compare the performance. Later, implementing federated learning to this model which also uses a blockchain concept for securing the data.
A Machine Learning approach for fall detection and daily living activity recognition
June 1, 2021 – October 1, 2021
Tested the performance of four algorithms for classifying human activities. Used acceleration and angular velocity data from two public databases to recognize seven different activities, including falls and activities of daily living. From the acceleration and angular velocity data, we extract time- and frequency-domain features and provide them to a classification algorithm. These algorithms are the artificial neural network (ANN), K-nearest neighbors (KNN), quadratic support vector machine (QSVM), and en semble bagged tree (EBT). New features that improve the performance of the classifier are extracted from the power spectral density of the acceleration. In the first step, only the acceleration data are used for activity recognition. In a second step, we extract features from the autocorrelation function and the power spectral density of both the acceleration and the angular velocity data, which improves the classification accuracy.
Certificate of Achievement: Freedom With AI Masterclass
Freedom With AI
June 25, 2026 – Present
Cultural Fit Analysis
The candidate's background is heavily skewed towards education and training in AI and Robotics, rather than direct industry experience in an ML Engineer role. While the projects demonstrate relevant technical understanding, the professional experience is primarily in teaching and curriculum development. This suggests a potential cultural fit challenge for a pure engineering team focused on product delivery, but could be a strong asset in roles involving knowledge transfer, mentorship, or R&D with an educational component. The diversity of roles (trainer, administrator, consultant) indicates adaptability, but the core focus remains on education.
Soft Skills & Operational Fit
The candidate demonstrates strong communication and training skills through their extensive experience as a trainer and educator. Their roles involve curriculum development, academic advising, and mentorship, indicating good interpersonal and leadership qualities. The project descriptions are clear and detailed, suggesting effective written communication. However, the primary experience is in education and training rather than direct ML engineering product development, which might require an adjustment period for operational fit in a pure engineering role.