Arman Amandykuly

Co-Founder & Lead Backend Engineer

Designed and deployed a production-grade, multi-service backend architecture using FastAPI to track and analyze user-generated content metrics in real-time. Implemented an asynchronous task processing pipeline using Celery and RabbitMQ to handle heavy data ingestion, distributed workloads, and background analytical workers. Optimized data caching and session management layers by integrating Redis, significantly reducing database load and improving API response times. Containerized the entire infrastructure using Docker and managed deployments through Rancher, ensuring reliable microservice orchestration and seamless environment scaling.

• Optimized data caching and session management layers by integrating Redis, significantly reducing database load and improving API response times

Android Developer (Freelance)

Built GovTest — a mobile app for civil-service exam preparation — using Kotlin and Firebase Firestore with real-time data sync. Contributed backend functionality for Mardan Boutique e-commerce platform and iftar.kz web project.

Software Engineer

Delivered automated control software for a biogas station spanning 8 containers across 2 locations, replacing manual monitoring and reducing operator intervention. Generated and graded laboratory works, quizzes and exams. Engineered a custom coroutine-based concurrency system in C/C++ on Arduino to manage 6+ async I/O streams (sensors, 220V motors, WiFi, Nextion display) without an RTOS. Integrated real-time sensor telemetry via API, enabling remote monitoring of station parameters.

• Delivered automated control software for a biogas station spanning 8 containers across 2 locations, replacing manual monitoring and reducing operator intervention

Research Assistant

Achieved 96% accuracy on inverse kinematics prediction by designing and training a neural network to replace analytical solvers, reducing computation time for real-time robot control. Formalized the full Denavit-Hartenberg forward kinematics model for a multi-DOF InMoov robotic arm; results published in a peer-reviewed journal (EEJP, 2022). Built a 3D simulation model to validate joint-angle predictions, enabling verification without physical hardware.

• Achieved 96% accuracy on inverse kinematics prediction by designing and training a neural network to replace analytical solvers, reducing computation time for real-time robot control
• Formalized the full Denavit-Hartenberg forward kinematics model for a multi-DOF InMoov robotic arm; results published in a peer-reviewed journal (EEJP, 2022)

Teaching Assistant

Instructed programming labs in C++ and Python for a cohort of 30 students, generating coursework and grading exams. Held weekly office hours and produced written guides, reducing repeated questions during lectures.

• Reduced repeated questions during lectures by producing written guides