Dias Bakhtiyarov, Developer in Almaty, Almaty Province, Kazakhstan
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Dias Bakhtiyarov

Verified Expert  in Engineering

Bio

Dias is a versatile, self-taught Python developer with nine years of experience. He is known for his ability to quickly learn and adapt to new technologies. His expertise spans system design, deep learning, geospatial data processing, time-series forecasting, black-box optimization, vehicle routing, discrete event simulation, web scraping, REST API development, and more. Dias is driven by a passion for solving problems and building solutions that create real-world value.

Portfolio

Amgreat North America
PyTorch, Data Engineering, Python, Scikit-learn, TensorFlow, Plotly, PostgreSQL...
Kwan Ting Chang
Natural Language Processing (NLP), Artificial Intelligence (AI), Llama 2...
Institute of Smart Systems and Artificial Intelligence
Algorithms, Docker, Python 3, FastAPI, Python, SQL, Chatbots...

Experience

  • Python - 7 years
  • NumPy - 5 years
  • PostgreSQL - 4 years
  • PyTorch - 4 years
  • SQLAlchemy - 4 years
  • Docker - 4 years
  • Pandas - 4 years
  • Deep Learning - 3 years

Availability

Part-time

Preferred Environment

Docker, Python, RabbitMQ, PostgreSQL, MLflow, Selenium, Vehicle Routing, Telegram Bots, FastAPI, OpenAI

The most amazing...

...thing I’ve created was a digital twin of a vehicle dispatcher and router to simulate last-mile delivery operations and train RL agents.

Work Experience

Data Engineer (via Toptal)

2023 - PRESENT
Amgreat North America
  • Developed a distributed training of ML models for time-series forecasting.
  • Architected interactive Plotly dashboards for business analytics.
  • Built tools to crawl various social networks.
Technologies: PyTorch, Data Engineering, Python, Scikit-learn, TensorFlow, Plotly, PostgreSQL, Machine Learning, Data Science, Pandas, Deep Learning

AI Expert (via Toptal)

2023 - 2024
Kwan Ting Chang
  • Developed a distributed speech-to-text and text-to-speech processing pipeline with deployment to production.
  • Implemented tools for managing and processing LLM conversations.
  • Designed a system of dockerized microservices architecture.
Technologies: Natural Language Processing (NLP), Artificial Intelligence (AI), Llama 2, Language Models

Back-end Maintenance Engineer

2022 - 2023
Institute of Smart Systems and Artificial Intelligence
  • Developed Telegram polling bot for data labeling of Kazakh and Turkish speech corpus.
  • Extracted audio and captions data from YouTube videos as part of data collection and aggregation for text-to-speech model development.
  • Maintained existing services and containerized new services for smooth operation on production servers.
Technologies: Algorithms, Docker, Python 3, FastAPI, Python, SQL, Chatbots, Natural Language Processing (NLP)

Core AI Engineer

2021 - 2023
Solai
  • Developed a simulation engine for last-mile delivery business operations involving capacitated multi-agent vehicle routing problems with time window constraints.
  • Created a continuous real-time dispatcher system using deep learning technologies and meta-heuristic optimization algorithms.
  • Created a discrete simulation framework with support for stochastic event-driven processes.
Technologies: Python 3, PyTorch, SimPy, Graph Neural Networks, DGL, OpenAI GPT-3 API, OpenAI API, OpenAI GPT-4 API, ChatGPT, Artificial Intelligence (AI), Chatbot Conversation Design

Back-end Web Developer and AI Engineer

2021 - 2022
Solai
  • Developed an asynchronous high-performance web back end using FastAPI, SQLAlchemy, Gunicorn, Starlette, and Pydantic.
  • Implemented geohashing as a caching layer for external map API calls to decrease operating costs.
  • Developed a role-based access control system with a hierarchical domain structure.
Technologies: FastAPI, SQLAlchemy, Docker, Amazon Web Services (AWS), Gunicorn, Starlette, Pydantic, Ansible, OpenStreetMap, Swagger, Redoc, Python, Machine Learning, Data Analysis, Deep Learning, Time Series Analysis, Amazon EC2, Amazon S3 (AWS S3), SQL

Python Software Developer and ML Engineer

2018 - 2021
Egistic
  • Built a core geospatial image processing pipeline for high-performance distributed provision of Sentinel-2 remote sensing imagery products to thousands of customers.
  • Designed and developed a dynamic weather data acquisition system using NASA’s NOAA service.
  • Created tools for automated deployment of services to production using Ansible, GitHub, and Docker.
  • Developed ML-based crop yield prediction service based on terrain profile (DEM), soil type, soil pH levels, historical time-series weather data, and remote sensing imagery products.
  • Built a deep learning-based model for crop field boundary delineation.
Technologies: Python, NumPy, Pandas, Bash, Docker, Ansible, Redis, Memcached, PostGIS, PostgreSQL, RabbitMQ, Celery, Docker Compose, Markdown, PyTorch, PyTorch Lightning, Machine Learning, Data Analysis, Data Science, Deep Learning, Computer Vision, Time Series Analysis, Big Data, Trend Forecasting, Neural Networks, Time Series, Amazon EC2, Amazon S3 (AWS S3), Convolutional Neural Networks (CNNs), Data Engineering, SQL, Image Processing, Image Analysis, Computer Vision Algorithms

Graduate Research Assistant

2017 - 2019
Nazarbayev University
  • Simulated operation of wireless sensor framework consisting of autonomous unmanned aerial vehicles (UAV) and ground-based sensors for energy-efficient seismic data collection and multi-agent routing.
  • Implemented digital image correlation and image processing of soil surface deformations due to underground soil-pipe interaction.
  • Implemented UAV autonomous navigation using simultaneous localization and mapping (SLAM).
Technologies: Python, Pandas, NumPy, Raspberry Pi, Bash, SSH, Data Analysis, Neural Networks, Multiprocessing

Experience

Large-scale Data Acquisition and Processing Pipeline for Deep Learning Research

Geo-spatial image processing pipeline for high-performance distributed provisioning of Sentinel-2 satellite-based imagery products to customers.

MY ROLE
• Developed a scalable and distributed acquisition pipeline for Sentinel-2 satellite-based multi-spectral imagery data using Celery, RabbitMQ, and Redis.
• Implemented grouped execution of chained tasks with dynamic graph structure and runtime configurability using Celery and RabbitMQ.
• Created hybrid scalable Redis disk-based results storage back end for RabbitMQ and Celery tasks.
• Implemented cloud making, temporal difference, and spectral segmentation of multi-spectral satellite imagery data using GDAL, NumPy, SciPy, and Pandas.

Transportation Management System

Deep RL-based next-generation transportation management system for last-mile delivery.

MY ROLE
• Developed a core back-end API codebase.
• Created a custom multi-role-based access control system with a hierarchical domain structure.
• Automated generation of input and output response body schemas for API requests using Pydantic and ReDoc.
• Made an asynchronous data access layer using SQLAlchemy, asyncpg, and PostgreSQL.
• Created asynchronous background tasks using asyncio.
• Wrote API documentation using Swagger and ReDoc.

Meteo Data Collector for Precision Farming

Data acquisition product for providing farmers with real-time weather monitoring service. I was the core back-end developer of the service. I significantly reduced database size while maintaining a high-read performance and minimum latency.

Education

2019 - 2021

Progress Toward PhD in Remote Sensing, Geospatial Data, and Computer Vision

Nazarbayev University - Astana, Kazakhstan

2015 - 2017

Master's Degree in Structural, Civil, and Environmental Engineering

Korea Advanced Institute of Science and Technology (KAIST) - Daejeon, Republic of South Korea

2011 - 2015

Bachelor's Degree in Civil Engineering

Nazarbayev University - Nur-Sultan, Kazakhstan

2010 - 2011

Undergraduate Preparatory Certificate in English

University College London - London, UK

2005 - 2010

High School Diploma with Golden Medal in Education

Kazakh-Turking High School - Almaty, Kazakhstan

Certifications

FEBRUARY 2022 - PRESENT

Codility Golden Award for the Year of the Tiger Challenge

Codility

FEBRUARY 2022 - PRESENT

Artificial Intelligence Foundations: Machine Learning

LinkedIn

Skills

Libraries/APIs

Pandas, NumPy, PyTorch, PyTorch Lightning, SQLAlchemy, GDAL, GDAL/OGR, LSTM, OpenAI API, Pydantic, Asyncio, Scikit-learn, TensorFlow

Tools

PyCharm, Celery, Docker Compose, AutoCAD, ChatGPT, Ansible, RabbitMQ, CAD, Redoc, Uvicorn, 3ds Max, Plotly

Languages

Python, Bash, SQL, Markdown, Python 3

Storage

PostGIS, PostgreSQL, Redis, Memcached, Database Structure, Amazon S3 (AWS S3)

Platforms

Docker, Raspberry Pi, Amazon Web Services (AWS), Amazon EC2

Frameworks

Starlette, Swagger, Flask, SimPy, Selenium

Other

SSH, Machine Learning, Programming, Deep Learning, Neural Networks, Data Engineering, Convolutional Neural Networks (CNNs), Image Processing, Optimization, Artificial Intelligence (AI), Algorithms, Data Analysis, Data Science, Computer Vision, Time Series Analysis, Trend Forecasting, Time Series, Image Analysis, Computer Vision Algorithms, OpenAI GPT-3 API, Chatbots, OpenAI GPT-4 API, OpenAI, Documentation, Genetic Algorithms, Evolutionary Algorithms, Finite Element Analysis (FEA), Finite Element Method (FEM), FastAPI, Gunicorn, OpenStreetMap, MkDocs, Data Structures, Multiprocessing, Big Data, Graph Neural Networks, DGL, Chatbot Conversation Design, Natural Language Processing (NLP), MLflow, Vehicle Routing, Telegram Bots, Physics, Self-directed Learning, English, Turkish, Geospatial Data, Llama 2, Language Models

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