COMMUNITYAskapro

Leading R&D on Biometric and Driving Behavior Systems

  • Led development of face and license plate analytics across 4 engineering teams.

  • Built ML models for driver identification using sensor data (RF, MLP, DT, KNN).

  • Researched video-based action recognition (publication under review).

  • Created a facial recognition API using FastAPI and OpenVINO Model Server.

  • Supported academic research with practical deployments and experiments

Talgat worked on this case as the at Nception Labs.
Machine learning
Team management
R&D
Computer Vision
Mobility Analytic
Automotive
Central Asia
Face recognition system
License plate recognition system
Driving behavior ML model
Python
OpenVINO
FastAPI
scikit-learn
Airflow
Internal product
Academia
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Talgat

Computer Vision Engineer at Sergek Technologies

Talgat's cases
Performance Optimisation
Face Recognition
Surveillance
Smart Cities
Kazakhstan
Multi-camera face recognition system
Real-Time Video Analytics
Deploying Real-Time Face Recognition Across Multiple Cameras

  • Trained and deployed face detection and feature extraction models to production.

  • Created APIs and inference pipelines using Triton Inference Server and TensorRT.

  • Designed a video stream processing system handling input from multiple cameras.

  • Increased vector search performance 20x by optimising FAISS-based similarity search.

  • Built dockerised infrastructure for scalable deployment.

Robotics
Computer Vision
Sensor Fusion
Research
Embedded Systems
Kazakhstan
Computer vision app
Human-Machine Interfaces & Vision Systems for Robotics

  • Led more than 5 research and development projects focused on building human-machine interfaces to control robotic systems via Android apps, WinForms applications, and console tools.

  • Conducted research in robotic control and trajectory planning, contributing to academic advancements and real-world implementations.

  • Worked on sensor fusion tasks involving PIR, sonar, camera, touch, and gyroscope inputs — integrating and processing data from multiple sources.

  • Completed two projects using microcontrollers and single-board computers such as Arduino, PIC32, Raspberry Pi, and Jetson TK1.

  • Delivered two working computer vision applications involving object recognition, emotion detection, and a “leader-following” system (robot tracking a moving subject).

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Similar cases

Machine learning
Team management
R&D
Computer Vision
Mobility Analytic
Automotive
Central Asia
Leading R&D on Biometric and Driving Behavior Systems

  • Led development of face and license plate analytics across 4 engineering teams.

  • Built ML models for driver identification using sensor data (RF, MLP, DT, KNN).

  • Researched video-based action recognition (publication under review).

  • Created a facial recognition API using FastAPI and OpenVINO Model Server.

  • Supported academic research with practical deployments and experiments

Team management
Forecasting Models
Backend Development
SaaS
Infrastructure
Russia
Website
Designing Core Backup Library and Forecasting Storage with ML

Led a team of 3 developers in designing and maintaining RuBackup’s software ecosystem.

Designed and implemented a modular Python library for backup integrations, now published on PyPi.

Released the first stable version of the web interface and REST API for RuBackup’s backup control system.

Developed and deployed an autoregressive model using statsmodels to predict remaining storage capacity.

Formalised internal coding standards, GitLab practices, and documentation processes to improve code quality and maintainability.

Product strategy
Stakeholder management
Tech knowledge
Team management
Project management
Research and Discovery
Agile
Developed projects for top-tier companies.

Managing cross-functional teams and facilitating collaboration across diverse program positions. I act as a bridge between technical AI teams and various business units ensuring seamless integration and deployment of AI solutions. I manage third-party applications to make sure AI tools work smoothly, using tools like EazyBI to make key metrics, Google Sheets Excel and specific charts for Agile teams.

Tech knowledge
Team management
Data analysis
Research and Discovery
Agile
AI
Education
Developed Versatile Time Series Model

Over a three-year period, we worked closely with one of Russia's largest banks to successfully establish a robust AI monitoring system. This system operates with precision to swiftly address system failures by pinpointing their root causes and making well-informed decisions. The model expanded the scope of forecasted indicators to a total of 50,000, achieving an 85% reduction in false positive alerts. It also delivered a 20% improvement in precision compared to previous methodologies.

Tech knowledge
Team management
Data analysis
Research and Discovery
Agile
AI
Education
Developed a backend AI monitoring system for detecting anomalies

Led a team to develop an AI monitoring system that was capable of detecting anomalies in various business indicators. By closely working with one of Russia's largest banks over a span of 3 years, we designed and implemented an advanced backend system dedicated to the detection of anomalies across a wide spectrum of critical business metrics. This system significantly decreased the time and effort previously invested in manual incident detection and enhanced operational efficiency.

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