Denis is available for hire
Hire DenisDenis Volk
Verified Expert in Engineering
Data Science Developer
Zürich, Switzerland
Toptal member since March 18, 2020
Bio
Denis is a senior full-stack AI engineer and data scientist, highly skilled in modern generative tech (GPT-4, Midjourney, and more), machine learning, ETL pipelines, data analysis, mathematical modeling, big data, and MLOps. He has a PhD in mathematics, and his data science expertise includes probabilistic risk modeling, revenue forecasting, geospatial data analysis, handwriting recognition, anomaly detection in time series, data engineering, and team leading.
Portfolio
Sema Technologies, Inc
Back-end, Machine Learning, Data Pipelines, Amazon Web Services (AWS)...
OkGPT
Python 3, Telegram Bot API, Telegram Bots, Telegram Messenger API, Asyncio...
Generative Tech Startup (ChatGPT)
Pandas, Big Data, Generative Pre-trained Transformers (GPT)...
Experience
Availability
Full-time
Preferred Environment
Jupyter, Bash, Git, Python, MacOS, Visual Studio Code (VS Code), Amazon Web Services (AWS)
The most amazing...
...system I've built was an end-to-end cloud ML solution for highly accurate house rental price prediction and risk estimation.
Work Experience
Back-end Engineer
2024 - 2024
Sema Technologies, Inc
- Trained a custom BERT deep neural network to identify AI-generated software code.
- Created a data pipeline to prepare model training data from open source code repositories on GitHub.
- Architected a test suite for classification models, including validation datasets and testing procedures.
- Deployed the classification models to production and containerized microservices in AWS cloud.
Technologies: Back-end, Machine Learning, Data Pipelines, Amazon Web Services (AWS), Data Management, SQL, ETL, Generative Artificial Intelligence (GenAI), Natural Language Processing (NLP), Visual Studio Code (VS Code), Amazon SageMaker, Amazon EC2, Amazon EC2 API, BERT, Custom BERT, Large Language Models (LLMs), Large Language Model Operations (LLMOps), Python 3, Python, Feature Engineering, Machine Learning Operations (MLOps), Text Classification, Deep Learning, Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), Minimum Viable Product (MVP)
Founder | CTO
2023 - 2024
OkGPT
- Created OkGPT, an AI personal assistant messenger bot. It allows users to interact with the most advanced AI through voice and text while offering additional productivity features and integrations.
- Implemented RAG and structured queries to make the assistant better grounded in the user's documents and background information to reduce hallucinations.
- Integrated multiple APIs, including Telegram, OpenAI, Google, Redis, Amplitude, Datadog, and more.
- Supervised three team members and a few external collaborators.
- Optimized the code to parallelize user queries' processing to increase the bot performance by magnitude.
- Implemented a complex subscription system with multiple tiers, referral links, discounts, and payment options.
- Set up continuous integration/deployment procedures.
- Added support for most of the languages in text and voice messages.
- Implemented agents to enable web search and improve the assistant's reasoning.
Technologies: Python 3, Telegram Bot API, Telegram Bots, Telegram Messenger API, Asyncio, Python Asyncio, Async/Await, OpenAI GPT-4 API, OpenAI GPT-3 API, Generative Pre-trained Transformer 3 (GPT-3), Generative Pre-trained Transformers (GPT), Speech to Text, Google Speech API, Text to Speech (TTS), Speech Recognition, Speech Synthesis, Natural Language Processing (NLP), Deep Learning, Google API, Google APIs, AIOps, Machine Learning Operations (MLOps), Python, Datadog, Amplitude, Selenium, Selenium API, Redis, PostgreSQL, SQLAlchemy, Docker, Docker Compose, Railway, Poetry, Mypy, Flake8, CI/CD Pipelines, Git, GitHub, API Hooking, Apify SDK, Web Search, Cloud, Continuous Deployment, Continuous Integration (CI), Continuous Delivery (CD), Dashboards, User Monitoring, Data Analytics, Analytics, Customer Retention, User Retention, PDF Scraping, Databases, Large Language Models (LLMs), CircleCI, Slack API, Unit Testing, Pytest, pylint, Asynchronous I/O, Coroutines, Google Cloud, Generative Artificial Intelligence (GenAI), Regular Expressions, Linux, SciPy, Metabase, Stripe API, Stripe Payments, Stripe, Billing, MongoDB, MongoDB Shell, LangChain, Llama 2, Retrieval-augmented Generation (RAG), Large Language Model Operations (LLMOps), Minimum Viable Product (MVP)
AI Engineer
2023 - 2023
Generative Tech Startup (ChatGPT)
- Developed an MVP of an app to query enterprise data in natural language. Given access to a database and a question in natural language about the data, the app would output the answer as a plot or a small table.
- Engineered and fine-tuned the prompts to improve the quality and correctness of SQL code generation.
- Created an automatic annotator for the database columns and the final table.
Technologies: Pandas, Big Data, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Text Generation, Code Generators, SQL, Language Models, Fine-tuning, CSV, ChatGPT, OpenAI, API Integration, OpenAI GPT-3 API, Chatbots, Databases, Generative Artificial Intelligence (GenAI), Regular Expressions, Linux, SciPy, LangChain, Llama 2, Data Scraping, Retrieval-augmented Generation (RAG), Large Language Model Operations (LLMOps), Minimum Viable Product (MVP)
Senior Machine Learning Engineer
2023 - 2023
Blockchain Security Company
- Created a machine learning model to automatically detect malicious smart contracts before they can cause harm.
- Built a visualization tool for model output to audit its decisions.
- Deployed the model to AWS cloud platform as a Lambda serverless function.
Technologies: Blockchain, Ethereum Smart Contracts, Smart Contracts, AWS Lambda, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Linear Regression, Decision Tree Regression, CSV, Databases, Linux, SciPy
Senior Python Engineer
2022 - 2023
Turn LLC
- Developed an app that translated text in English to a special pseudo-phonetic alphabet.
- Acted as a consultant to help define the deliverables and then the overall architecture of the translation tool.
- Helped to define text annotation requirements and oversaw the annotation process.
Technologies: Programming, Python, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), CSV, Regular Expressions, Linux, SciPy, Minimum Viable Product (MVP)
Freelance Senior Data Scientist and Data Engineer
2021 - 2022
Israel-based HR Tech Startup
- Created a BI dashboard to Query and summarize a vast amount of semi-structured data.
- Set up and tuned an Elasticsearch cluster and Kibana on AWS cloud.
- Developed an ETL pipeline to ingest a terabyte of raw data into Elasticsearch.
- Architected and directed the creation of a core Similarity engine to score candidates.
- Created a Big Data pipeline in Databricks and Spark to enrich the input data and prepare the features for ML.
- Used pre-trained NLP deep neural networks to create semantic text embeddings, which significantly increased the Similarity engine output results.
- Developed a score using Spark GraphX to measure the company's attractiveness in the job market.
- Prepared custom deep learning models to build richer embeddings, including various data sources and metadata.
- Led communications with external data providers and created infrastructures to interface with their APIs.
- Drove implementation of best DevOps and MLOps practices to improve reliability and reproducibility of ETL, feature generation, models' training, and inference subsystems.
Technologies: Elasticsearch, Kibana, Amazon S3 (AWS S3), Amazon EC2, AWS CLI, Visual Studio Code (VS Code), Jupyter Notebook, Flask, Databricks, Spark, Delta Lake, Parquet, JSON, Star Schema, FAISS, Apache Spark, User-defined Functions (UDF), Spark SQL, Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Machine Learning Operations (MLOps), Python, Python 3, Spark ML, Jira, Monday.com, Big Data, Data Science, Data Engineering, Machine Learning, Data Wrangling, ETL, GraphX, Cloud, Data Analytics, Data Integration, MongoDB, Deep Learning, Objectives & Key Results (OKRs), Keras, Algorithms, XGBoost, Predictive Modeling, Programming, Recommendation Systems, Generative Pre-trained Transformer 3 (GPT-3), Hugging Face, PostgreSQL, Azure Databricks, Data Analysis, Linear Regression, Random Forests, Random Forest Regression, Data Modeling, Pattern Matching, Language Models, Data Matching, LSTM, Forecasting, Amazon Web Services (AWS), Data Build Tool (dbt), DevOps, Mathematical Analysis, Full-stack, Architecture, PyTorch, CSV, Amazon Machine Learning, Amazon DynamoDB, Amazon Comprehend, API Integration, Databases, Regular Expressions, Linux, SciPy, Minimum Viable Product (MVP)
Freelance Senior Data Scientist and Data Engineer
2020 - 2021
US-based Ops/Tech Startup (via Toptal)
- Built a foundational end-to-end machine learning solution that predicts fair prices of real-estate properties, thus eliminating a need for manual assessment and enabling the company to run its business by providing quick responses to its customers.
- Designed and implemented an automatically refreshing ETL pipeline that injects, cleans, joins, and enriches new data from AWS S3 storage daily.
- Developed an interpretable machine learning model with Scikit-learn, CatBoost, Lifelines, FBProphet, FAISS, and SHAP that consists of several submodels and satisfies business monotonicity constraints.
- Set up a continuous machine learning procedure for daily model retraining and redeploying based on the newly collected data.
- Designed and implemented an automatic model promotion mechanism to ensure that models produced via the daily retraining process get deployed to production only if they have sufficiently good performance metrics and satisfy business constraints.
- Created a historical data simulation system to generate synthetic data before the company’s launch and enable backtesting capabilities.
- Architected and built the required infrastructure in AWS cloud: EC2 instances and VPCs, Docker environments for development, testing, and production, an Airflow pipeline for ETL and ML, and MLFlow model storage.
- Created various dashboards for data exploration and data quality management, model performance monitoring, and visualized predictions.
- Supervised other data science team members and coordinated with the engineering team.
Technologies: Amazon S3 (AWS S3), Amazon RDS, Amazon EC2, AWS Elastic Beanstalk, AWS Lambda, Amazon SageMaker, Grafana, MLflow, Apache Airflow, Metabase, Amazon CloudWatch, AWS CloudFormation, Airtable, Amazon Elastic Container Registry (ECR), CircleCI, Git, Docker, Docker Compose, Python, Dagster, Jupyter, Jupyter Notebook, Time Series, Predictive Modeling, Analysis, Dask, GIS, Machine Learning, Data Science, Data Engineering, Jira, AWS CLI, Flask, SQL, PostgreSQL, SQLAlchemy, Pandas, NumPy, CatBoost, Scikit-learn, Data Pipelines, Data Architecture, ETL, Data Visualization, Data Validation, Data Wrangling, Modeling, Amazon Elastic Block Store (EBS), Solution Architecture, Software Architecture, Cloud, Data Analytics, Data Integration, Data Inference, Business Intelligence (BI), ETL Tools, APIs, Python 3, Machine Learning Operations (MLOps), ARIMA, Algorithms, XGBoost, Programming, GeoPandas, Data Analysis, Linear Regression, Random Forests, Random Forest Regression, Data Modeling, Data Matching, Forecasting, Amazon Web Services (AWS), DevOps, Mathematical Analysis, Technical Leadership, Full-stack, Architecture, CSV, Amazon Machine Learning, API Integration, Databases, Regular Expressions, Linux, SciPy, Statistics, Data Scraping, Minimum Viable Product (MVP)
Data Science Evangelist
2020 - 2020
Skillbox
- Reviewed and improved core courses in mathematics, data science, and machine learning.
- Supervised the creation of new courses, including video lectures and exercises, on Data Science, Analytics, SQL, Power BI, and Tableau.
- Recruited, interviewed, and screened lecturers and tutors for new courses.
Technologies: Pandas, Scikit-learn, Data Science, Machine Learning, Data Science, Mathematics, Python 3, Algorithms, XGBoost, Linear Regression, Random Forest Regression, Data Analysis
Lecturer
2019 - 2020
Netology
- Prepared and lectured a course on calculus for data scientists.
- Developed and presented a course on linear algebra for data scientists.
- Designed, prepared, and lectured a course on probability theory for data scientists.
Technologies: Data Science, Machine Learning, Data Science, Mathematics
Senior Data Scientist
2017 - 2019
KPMG
- Created a machine learning model that predicted revenues for a retail store chain based on store location, local demographic data, GIS features, seasonality, and other factors.
- Developed and deployed an interpretable machine learning model that scored B2B customers for payment default risks and provided explanations for the scores. The model massively reduced workload for weekly risks assessment.
- Built a probabilistic Bayesian machine learning model to predict which apartment buildings still under construction would fail to be commissioned in time. The model helped reduce the funds needed to hedge risks by two times.
- Developed and deployed NLP models to automatically label a vast body of housing contracts by contract type and extract contractor party names, address entities, and other attributes.
- Constructed and deployed a model to predict the problematic clogging of the evaporator in a chemical factory. This allowed for the timely preemptive service of the unit before it broke down, saving millions of dollars in production time.
- Led and mentored a team of junior and middle data scientists in the projects mentioned above.
- Communicated with clients, ensuring business goals were correctly translated into data science and machine learning tasks—explained insights and models to clients.
- Architected ETL pipelines, including data acquisition, data ingestion, merging internal and external datasets, data cleaning and validation, data transformation, and feature engineering on several distinct projects.
- Designed model performance metrics and their measurement protocols on several distinct projects.
- Developed an ML system for a retail bank to recommend bank products to clients based on their past transactions' patterns. This included building an ETL pipeline and an ML recommendation system.
Technologies: Data Science, Data Science, Machine Learning, Big Data, Artificial Intelligence (AI), Apache Hive, Hadoop, Spark, Dash, Plotly, Pandas, Scikit-learn, Git, Jupyter Notebook, Python, PyTorch, TensorFlow, PySpark, Spark SQL, Data Wrangling, Docker, Docker Compose, SQL, Data Analysis, CatBoost, XGBoost, Time Series, Anomaly Detection, Analysis of Variance (ANOVA), Keras, CircleCI, Bayesian Statistics, Data Analytics, Business Intelligence (BI), Solution Architecture, Software Architecture, Data Integration, Data Inference, ETL, ETL Tools, APIs, Deep Learning, Python 3, Machine Learning Operations (MLOps), Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Deep Neural Networks (DNNs), Apache Spark, OCR, ARIMA, Algorithms, Predictive Modeling, Programming, Image Processing, Recommendation Systems, PostgreSQL, Azure, GeoPandas, Linear Regression, Random Forests, Random Forest Regression, Data Modeling, Computer Vision, Handwriting Recognition, Language Models, SARIMA, LSTM, Forecasting, R, Prefect, DevOps, Statistical Analysis, Google Cloud Platform (GCP), Mathematical Analysis, Technical Leadership, Full-stack, Architecture, Manufacturing, CSV, Regular Expressions, Linux, SciPy, Statistics, Markov Model, Data Scraping, Minimum Viable Product (MVP)
Centre for Advanced Studies (CAS) - Postdoctoral Researcher
2015 - 2017
National Research University — Higher School of Economics
- Invented a novel mathematical method for cross-frequency synchronization analysis in the human brain.
- Implemented the method as a MATLAB toolbox and ran tests confirming that the results agreed with previously known scientific data.
- Prepared and published the method and findings in a top-level journal.
- Supervised the master's degree projects of several students.
- Lectured a master's-level course on computational neuroscience.
Technologies: Brain-computer Interface, Python, MATLAB, Data Science, ETL, Data Preparation, Signal Processing, Medical Imaging, EEG, EEG Libraries for Python, Software Architecture, Solution Architecture, Research, Science, Data Science, Life Science, APIs, Mathematical Analysis, Statistics
ERC Advanced Grant Postdoctoral Researcher
2013 - 2015
University of Rome (Tor Vergata)
- Discovered a new geometric phenomenon accountable for the rigidity of certain mathematical models related to heat conduction in crystals.
- Discovered a new stability property of attractors of multidimensional piecewise isometry maps related to Markov field models.
- Discovered that almost every interval translation map of three intervals is finite type.
- Prepared papers describing the findings and published them in high-ranking journals.
Technologies: Data Science, System Dynamics, Mathematics, Research, Science, Mathematical Analysis, Statistics
Visiting Scientist (Consultant in Mathematics and 3D Scanning)
2013 - 2015
Institute for Basic Research in Developmental Disabilities
- Created and verified mathematical models for the growth of blood vessels in the human placenta during the gestation period.
- Developed the protocol for 3D data collection, including 3D surface scanning and micro-CT scans of the specimen.
- Created an ETL pipeline to clean up and preprocess the collected samples.
- Analyzed the collected data, fitted the mathematical models, and interpreted the findings.
Technologies: Data Science, Scikit-learn, Data Processing, 3D Reconstruction, 3D Scanning, Data Science, Machine Vision, Python, MATLAB, ETL, Research, Science, Life Science, Medical Imaging, APIs, Algorithms, Programming, Data Analysis, Data Modeling, Computer Vision, Healthcare, Statistical Analysis, Mathematical Analysis, Statistics
Göran Gustafsson Postdoctoral Researcher
2012 - 2013
KTH Royal Institute of Technology
- Established that the rotation numbers of circle maps' semigroups define their generators.
- Discovered a fractal structure of attractors of piecewise isometry maps related to Markov field models.
- Prepared the papers describing the findings and published them in leading journals.
- Lectured a PhD-level course on the structural stability of dynamical systems.
Technologies: MATLAB, Data Science, System Dynamics, Mathematics, Research, Science, Mathematical Analysis, Statistics
Postdoctoral Researcher
2010 - 2012
SISSA
- Discovered a new class of dynamical systems that have persistent massive attractors.
- Established a deep relationship between skew product dynamical systems over Markov chains and nonlinear random walks.
- Prepared the papers describing the findings and published them in major journals.
Technologies: Data Science, System Dynamics, Mathematics, Science, Research, Mathematical Analysis, Statistics
Researcher and Software Engineer
2007 - 2007
Artec Group
- Designed and implemented biometric machine learning face recognition algorithms.
- Created and implemented statistical test procedures for new recognition algorithms.
- Developed calibration procedures from 3D laser and flash scanners.
- Refactored Windows32 code to make it cross-platform.
- Implemented and tuned 3D surface reconstruction algorithms.
- Applied software for barcode encoding and scanning.
- Managed the build server and was responsible for CI/CD process for our team.
Technologies: Data Science, Artificial Intelligence (AI), Neural Networks, Machine Learning, Git, MATLAB, wxWidgets, C++, 3D Reconstruction, Research, Software, Software Architecture, Solution Architecture, Algorithms, Programming, Image Processing, Computer Vision, Mathematical Analysis, Software Development, Computer Science
Researcher and Software Engineer
2004 - 2007
A4Vision
- Designed and implemented algorithms for face detection on a 2D image, facial features detection, and alignment on a 3D surface.
- Implemented and tuned 3D surface reconstruction algorithms.
- Built and implemented statistical test procedures for new machine learning algorithms for face recognition.
- Constructed and implemented a system for automatic test report generation based on log parsing.
- Implemented and managed the automated build system dedicated to the building server.
- Calibrated optical cameras and lasers for 3D scanners.
- Migrated the algorithmic core to an embedded platform.
Technologies: Git, Data Science, Artificial Intelligence (AI), Embedded Development, Neural Networks, Machine Learning, Subversion (SVN), MATLAB, C++, Machine Vision, 3D Reconstruction, Research, Software, Software Architecture, Solution Architecture, Algorithms, Programming, Image Processing, Computer Vision, Mathematical Analysis, Software Development, Computer Science
Intern
2000 - 2004
Parascript
- Developed a mathematical background for novel machine learning methods for handwritten text recognition.
- Implemented novel methods for handwritten text recognition as a C++ library.
- Presented my research at scientific conferences and seminars.
Technologies: Data Science, Artificial Intelligence (AI), Machine Learning, OpenCV, C++, OCR, Research, Science, Software, Algorithms, Image Processing, Linear Regression, Computer Vision, Handwriting Recognition, Language Models, Mathematical Analysis, Computer Science, Markov Model
Experience
House Rental Price Prediction
An end-to-end cloud ML solution for highly accurate house rental price prediction and risk estimation. The solution was foundational to a Bay Area-based startup with the goal of uberization of the house rental market. It included raw data ingestion, a complex ETL pipeline, a suite of predictive models, MLOps processes including CI/CD, model, data versioning, and production model monitoring. I supervised a few other engineers who joined the project later to further improve the system.
Revenue Prediction for Retail Store Chain
Built a machine learning model that predicted revenues for a retail store chain based on store location, local demographic data, GIS features, seasonality, and other factors. I was the tech lead in a group of data scientists who ran the whole cycle from data extraction, web scrapping, ETL, exploratory analysis, data preprocessing, feature engineering, machine learning, packaging the model as a standalone service, and implementing a dashboard.
Payment Default Risk Scoring
Built and deployed an interpretable machine learning model that scored B2B customers for payment default risks and provided explanations for the scores. The model massively reduced workload for weekly risks assessment. I was the tech lead in a group of data scientists who ran the whole cycle from data extraction, merging several different data sources, ETL, exploratory analysis, data preprocessing, feature engineering, machine learning, packaging, and deploying the model to the client' premises.
Probabilistic Model for Building Commission Times
Built a probabilistic Bayesian machine learning model to predict which apartment buildings still under construction would fail to be commissioned in time. The model helped reduce the funds needed to hedge risks by two times. In addition to typical data science project activities, which included data exploration, ETL, and ML, this project also involved setting up machinery for the explicit Bayesian inference of structured models using GPUs.
Education
2007 - 2010
Doctoral Degree in Mathematics
Lomonosov Moscow State University - Moscow, Russia
1999 - 2004
Master's Degree in Mathematics
Lomonosov Moscow State University - Moscow, Russia
Certifications
SEPTEMBER 2019 - PRESENT
Machine Learning Summer School 2019
Skolkovo Institute of Science and Technology
SEPTEMBER 2018 - PRESENT
DeepBayes Summer School 2018
National Research University—Higher School of Economics
SEPTEMBER 2017 - PRESENT
Advanced Scientific Programming in Python 2017
G-Node
APRIL 1997 - PRESENT
Russian Mathematical Olympiad, 2nd Prize, 3rd Overall
Russian Mathematical Olympiad Committee
Skills
Libraries/APIs
Pandas, NumPy, CatBoost, Scikit-learn, XGBoost, PyMC, LSTM, SciPy, SQLAlchemy, PyTorch, PySpark, OpenCV, wxWidgets, Dask, TensorFlow, Keras, Spark ML, GraphX, Telegram Bot API, Telegram Messenger API, Asyncio, Python Asyncio, Google Speech API, Google API, Google APIs, Selenium API, Mypy, Slack API, Stripe API, Stripe, Amazon EC2 API
Tools
Jupyter, Amazon SageMaker, Git, MATLAB, Plotly, ARIMA, SARIMA, AWS CLI, Jira, Confluence, Apache Airflow, AWS CloudFormation, Docker Compose, GIS, Spark SQL, Prefect, ChatGPT, PyCharm, Subversion (SVN), CircleCI, Amazon Elastic Block Store (EBS), Tableau, Grafana, Amazon CloudWatch, Amazon Elastic Container Registry (ECR), Kibana, GitHub, Pytest, pylint, MongoDB Shell
Languages
Python, Python 3, SQL, Bash, R, C++
Paradigms
ETL, Anomaly Detection, Business Intelligence (BI), Objectives & Key Results (OKRs), System Dynamics, DevOps, Continuous Deployment, Continuous Integration (CI), Continuous Delivery (CD), Unit Testing
Platforms
MacOS, Amazon EC2, Amazon Web Services (AWS), Jupyter Notebook, Linux, Docker, AWS Lambda, Visual Studio Code (VS Code), Apache Kafka, AWS Elastic Beanstalk, Google Cloud Platform (GCP), Databricks, Azure, Blockchain
Storage
Amazon S3 (AWS S3), Data Pipelines, Data Validation, Databases, Apache Hive, PostgreSQL, Data Integration, Google Cloud, MongoDB, Elasticsearch, JSON, Amazon DynamoDB, Datadog, Redis
Frameworks
Apache Spark, Hadoop, Spark, Flask, Selenium
Industry Expertise
Healthcare
Other
Data Science, Data Analysis, Machine Learning, Bayesian Inference & Modeling, Time Series, Regular Expressions, Mathematics, Data Science, Bayesian Statistics, Artificial Intelligence (AI), MLflow, Predictive Modeling, Data Visualization, Algorithms, GeoJSON, GeoPandas, APIs, Cloud, Data Analytics, Research, Natural Language Processing (NLP), Machine Learning Operations (MLOps), Programming, Linear Regression, Random Forests, Random Forest Regression, Data Modeling, Forecasting, Mathematical Analysis, Markov Model, CSV, Amazon Machine Learning, Generative Pre-trained Transformers (GPT), OpenAI GPT-4 API, CI/CD Pipelines, Large Language Models (LLMs), LangChain, Retrieval-augmented Generation (RAG), Minimum Viable Product (MVP), Neural Networks, Data Processing, Predictive Analytics, Statistics, Big Data, Data Engineering, Data Architecture, Analysis of Variance (ANOVA), Software Architecture, Solution Architecture, User-defined Functions (UDF), Deep Neural Networks (DNNs), Deep Learning, Hugging Face, Azure Databricks, Computer Vision, Language Models, Data Matching, Statistical Analysis, Software Development, Technical Leadership, Full-stack, Architecture, OpenAI, API Integration, OpenAI GPT-3 API, Chatbots, PDF Scraping, Generative Artificial Intelligence (GenAI), Llama 2, Data Scraping, Large Language Model Operations (LLMOps), Embedded Development, Brain-computer Interface, 3D Scanning, 3D Reconstruction, Vowpal Wabbit, Dash, Optimization, Amazon RDS, Metabase, Airtable, Dagster, Analysis, Data Wrangling, Modeling, Machine Vision, OCR, Data Inference, ETL Tools, Data Preparation, Signal Processing, Medical Imaging, EEG, EEG Libraries for Python, Science, Life Science, Software, Delta Lake, Parquet, Star Schema, FAISS, Recurrent Neural Networks (RNNs), Monday.com, Image Processing, Recommendation Systems, Generative Pre-trained Transformer 3 (GPT-3), Handwriting Recognition, Pattern Matching, Data Build Tool (dbt), Manufacturing, Computer Science, Ethereum Smart Contracts, Smart Contracts, Decision Tree Regression, Text Generation, Code Generators, DreamBooth, Stable Diffusion, ControlNet, Fine-tuning, Google Cloud Machine Learning, Amazon Comprehend, Telegram Bots, Async/Await, Speech to Text, Text to Speech (TTS), Speech Recognition, Speech Synthesis, AIOps, Amplitude, Railway, Poetry, Flake8, API Hooking, Apify SDK, Web Search, Dashboards, User Monitoring, Analytics, Customer Retention, User Retention, Asynchronous I/O, Coroutines, Stripe Payments, Billing, Back-end, Data Management, BERT, Custom BERT, Feature Engineering, Text Classification
Collaboration That Works
How to Work with Toptal
Toptal matches you directly with global industry experts from our network in hours—not weeks or months.
1
Share your needs
Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2
Choose your talent
Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3
Start your risk-free talent trial
Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.
Top talent is in high demand.
Start hiring