Rishab is available for hire
Hire RishabRishab Pal
Verified Expert in Engineering
Data Scientist and AI Developer
Bengaluru, Karnataka, India
Toptal member since November 10, 2022
Bio
Rishab is an experienced data scientist and machine learning (ML) expert with a strong passion for mathematics and artificial intelligence (AI). He has 6+ years of experience designing and developing end-to-end machine learning solutions that have been successfully deployed to production on cloud platforms. Rishab constantly invests in his skills to stay up-to-date with the latest advancements in the field. He is committed to applying his knowledge and expertise to develop impactful solutions.
Portfolio
Invisible Technologies
Prompt Engineering, Artificial Intelligence (AI)...
Tomorrow People
Prompt Engineering, AI Prompts, Artificial Intelligence (AI), APIs...
Solstice Health Inc
Python, Large Language Models (LLMs), Fine-tuning, ChatGPT, Mistral AI, Llama 2...
Experience
- Python - 7 years
- Natural Language Processing (NLP) - 7 years
- Data Science - 6 years
- Data Analytics - 6 years
- Statistical Modeling - 5 years
- Deep Learning - 5 years
- Software Deployment - 5 years
- DeepSeek - 1 year
Availability
Part-time
Preferred Environment
Machine Learning, Deep Learning, Data Science, Natural Language Processing (NLP), Computer Vision, Python, Artificial Intelligence (AI), Generative Artificial Intelligence (GenAI), Machine Learning Operations (MLOps)
The most amazing...
...accomplishment I've had was showcasing my project at CES—International Consumer Electronics Show, the technology sector's flagship trade fair, in 2019.
Work Experience
Prompt Engineer
2024 - PRESENT
Invisible Technologies
- Developed hallucination detection and content verification systems using LangChain agents and LangGraph, creating ensemble techniques that reduced inaccuracies by 65% while maintaining strict data privacy protocols for enterprise clients.
- Engineered prompt frameworks for claim extraction and verification using OpenAI Assistants and custom Python tooling, enabling automated quality assurance processes that scaled to process 30,000+ content pieces daily.
- Implemented and fine-tuned Llama models as verification agents, developing novel techniques for cost-effective factuality assessment that maintained 92% accuracy while reducing API costs by 72%.
Technologies: Prompt Engineering, Artificial Intelligence (AI), Natural Language Processing (NLP), Machine Learning, Data Science, Python, PyTorch, TensorFlow, Cloud
Prompt Engineer
2025 - 2025
Tomorrow People
- Developed and optimized enterprise-grade prompt engineering frameworks using LangChain and LangGraph, resulting in a 40% improvement in content generation quality for sales and marketing materials across multiple industry verticals.
- Led knowledge management initiatives including fine-tuning Llama models on domain-specific data, building structured knowledge graphs, and designing retrieval-augmented generation (RAG) systems that improved content relevance by 35%.
- Architected and implemented custom AI agent systems with CrewAI that enhanced sales workflows, integrating with Claude and ChatGPT via APIs while maintaining comprehensive documentation and prompt libraries.
- Collaborated with sales and marketing teams to analyze prompt performance metrics, conduct A/B testing on different prompt strategies, and iteratively refine AI solutions to meet specific business requirements.
Technologies: Prompt Engineering, AI Prompts, Artificial Intelligence (AI), APIs, API Integration, Claude, ChatGPT
AI Developer
2024 - 2024
Solstice Health Inc
- Extracted key medical claims from research documents and materials published by U.S. pharmaceutical companies, meeting FDA requirements.
- Used extracted data to create presentations for customers and clinicians, supporting sales pitches for new drugs.
- Built the solution on LangChain, with fine-tuned models and prompt engineering.
- Delivered over 90% successful claim extraction based on expert reviews and analysis.
Technologies: Python, Large Language Models (LLMs), Fine-tuning, ChatGPT, Mistral AI, Llama 2, Llama 3, Retrieval-augmented Generation (RAG), LangChain, AI Agents, DeepSeek, Claude, Gemini
AI Developer
2024 - 2024
FLIRT TECHNOLOGIES, LLC
- Developed AI-powered social media app features for personalized content creation using Stable Diffusion and Deepgram.
- Enabled near real-time immersive experiences with professional model photos, videos, and voice cloning.
- Used machine learning techniques like low-rank adaptation (LoRA) based on PEFT, model quantization, checkpoint merging, and other related approaches to achieve state-of-the-art (SOTA) optimization and performance.
Technologies: Artificial Intelligence (AI), Large Language Models (LLMs), Machine Learning, Stable Diffusion, OpenAI, Text to Image, Claude, Gemini
AI/ML Expert
2023 - 2024
Nolea Technology Ltd
- Built an MVP for the recommendation system to match healthcare companies with clinical specialists in mental healthcare, allied healthcare, orthopedics, etc.
- Re-engineered the existing recommendation engine using SOTA NLP approaches and machine learning models like named-entity recognition (NER), LayoutLM, and LayoutParser, and built custom doc embeddings.
- Designed a cloud-native trainable and scalable architecture, previously built on a rule engine that lacked robustness for their business model.
Technologies: Python, Scikit-learn, Pandas, Amazon Web Services (AWS), Artificial Intelligence (AI), Machine Learning, Recommendation Systems, Retrieval-augmented Generation (RAG), Generative Artificial Intelligence (GenAI), Azure AI Studio, ChatGPT, OpenAI Assistants API, OpenAI API, LangChain, Claude, Gemini
LLM Chatbot Developer
2023 - 2023
Revibe AI
- Completed a POC of a mental health assistant built on top of Voiceflow. It understands the user's mind, responds with appropriate custom suggestions from the given knowledge base, or engages in a general conversation like a human.
- Built the RAG-based chatbot that replicates the behavior of a neuroscientist and emphatically engages with the user during the conversation.
- Used LangChain to enrich the vector store, adding proprietary neuroscience and psychological data to generate a more relevant response from the LLMs.
Technologies: Generative Pre-trained Transformers (GPT), Python, Chatbot Conversation Design, API Integration, Natural Language Understanding (NLU), Natural Language Processing (NLP), Language Models, Artificial Intelligence (AI), Voiceflow, Cognitive Behavioral Therapy (CBT), Zapier, Machine Learning, Chatbots, Retrieval-augmented Generation (RAG), Generative Artificial Intelligence (GenAI), Azure AI Studio, LangChain, Integrated Development Environments (IDE), ChatGPT, OpenAI Assistants API, OpenAI API, AI Agents, Azure SQL Databases, Claude, Gemini
ML Expert | Data Scientist
2023 - 2023
Zachary Gidwitz
- Built a recommendation engine for an addiction recovery app that incorporates user demographics, mental state, and other metadata from app interaction to suggest quotes and prayers relevant to the user's state of mind.
- Fine-tuned the OpenAI GPT3 model and used Langchain to build a sequence of prompts where each preceding answer is included in the subsequent question. I added memory and linked the vector database quickly.
- Provided several offline and online metrics to track the live performance of the model and the recommendation system.
Technologies: Data Science, Machine Learning, Python, Recommendation Systems, Data Strategy, Data, Chatbots, OpenAI GPT-3 API, Jupyter, Cloud, OpenAI GPT-4 API, Chatbot Conversation Design, Integration, Programming, AI Programming, Data Cleaning, Unstructured Data Analysis, Airtable, Machine Learning Automation, Project Management, Statistical Analysis, Data Reporting, Data Cleansing, Prompt Engineering, Retrieval-augmented Generation (RAG), Generative Artificial Intelligence (GenAI), Azure AI Studio, LangChain, ChatGPT, OpenAI Assistants API, OpenAI API, AI Agents, Azure SQL Databases
Machine Learning Engineer
2023 - 2023
Odem Global Pty Ltd
- Trained causal language model (CLM) with DeepSpeed on Bittensor, a peer-to-peer open-source protocol powering a globally distributed decentralized neural network.
- Used a mountain dataset (800+ GB) to train generative LLMs using DeepSpeed, which has lower network validation loss and thereby increases the model's reward, benefiting the client.
- Contributed to hyper-parameter tuning and revamped the default PyTorch data loader for faster dataset loading. Also, validated the models' performance on the validation set to ensure it meets the desired performance metrics.
- Fine-tuned models like GPT-J-Neo, Llama, MPT, Vicuna, Koala, Cerebras, to mention a few.
Technologies: Language Models, Machine Learning, Fine-tuning, Causal Inference, DeepSpeed, OpenAI GPT-3 API, Jupyter, Cloud, Integration, Programming, AI Programming, Data Cleaning, Unstructured Data Analysis, Machine Learning Automation, Large Language Models (LLMs), Project Management, Regex, Statistical Analysis, Data Reporting, Data Cleansing, Prompt Engineering, Chatbots, Retrieval-augmented Generation (RAG), Azure SQL Databases
AI Expert
2023 - 2023
Rich Lemon Apps FZE LLC
- Developed a user sticker model using Stable Diffusion and ControlNet for a given style.
- Experimented with different configurations of parameters to understand the effect on the results and achieved state-of-the-art results.
- Explored different methods like checkpoint merging, trained a stickers model, and then fine-tuned that model on Portrait Face, and combined sticker style and user image training.
Technologies: Artificial Intelligence (AI), Deep Learning, Image Processing, Stable Diffusion, Computer Vision, ControlNet, 3D, Microservices Architecture, Data Scientist, Jupyter, Cloud, Integration, Programming, AI Programming, Data Cleaning, Machine Learning Automation, Image Analysis, Project Management, Statistical Analysis, Data Reporting, Data Cleansing, Prompt Engineering, Image Generation, Text to Image, Multimodal GenAI, OpenAI API, Azure SQL Databases
Senior ML Expert | Data Scientist
2021 - 2023
EagleView
- Revamped roof measurement automation for US houses by engineering a high-performance Line Detection model on aerial imagery to detect roof, wall, and ground lines from 28% to 59%.
- Developed segmentation models for roof and wall facet detection while handling obstruction, achieving 86% accuracy.
- Improvised complex PyTorch models to facilitate ONNX and TensorRT export for efficient deployment using NVIDIA Triton Server.
- Orchestrated microservices using Step Functions, AWS Lambda, and Amazon SageMaker endpoints, improving response time by 25%. This was followed by simplifying maintenance and support, focusing on maximizing code reuse.
Technologies: Python, Machine Learning Operations (MLOps), PyTorch, Python API, Docker, Computer Vision, GPU Computing, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Data Science, SQL, Neural Networks, Data Visualization, Unsupervised Learning, Code Review, Remote Team Leadership, Graphics Processing Unit (GPU), Transfer Learning, Transformers, Sequence Models, Image Retrieval, Image Recognition, Classification, Entity Extraction, Siamese Neural Networks, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNNs), TensorBoard, Generative Adversarial Networks (GANs), Linux, Language Models, Python 3, Recommendation Systems, Algorithms, XGBoost, Data Extraction, Data Manipulation, Snowflake, Analytics, MySQL, Amazon Web Services (AWS), Data Analysis, Version Control Systems, Communication, Data Engineering, Regression, RAPIDS, APIs, Amazon SageMaker, NoSQL, Image Search, AI Design, Jupyter Notebook, Flask, NVIDIA TensorRT, Microservices Architecture, Data Scientist, Jupyter, Cloud, Anomaly Detection, Integration, Programming, AI Programming, Bitbucket, Data Cleaning, Unstructured Data Analysis, Machine Learning Automation, Kubernetes, Amazon Elastic Container Service (ECS), Image Analysis, Large Language Models (LLMs), Project Management, Statistical Analysis, Data Reporting, Data Cleansing, You Only Look Once (YOLO), Databricks, Azure SQL Databases
Senior Software Engineer/Machine Learning (ML)
2020 - 2021
Airtel India
- Designed and built a chatbot, leveraging HuggingFace transformers with DeepSpeed to resolve queries for over 400 million of Airtel's monthly active users. Deployed successfully, handling over 10 million daily conversations with 85% accuracy.
- Improved the accuracy of face recognition, matching, and liveness-check models. Added face segmentation, blur and forgery detection, real-time blacklisting, and face search. Enhanced service efficiency and reduced manual effort by over 25%.
- Inculcated best MLOps practices on the team to lower technical debt and adapt workflow that requires little to no manual intervention for simulations, testing, deployment, and monitoring.
Technologies: Data Analytics, Statistical Modeling, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Python 3, Docker, Bash Script, Data Science, SQL, Optical Character Recognition (OCR), Neural Networks, Optimization, Clustering, Code Review, Remote Team Leadership, Graphics Processing Unit (GPU), Transfer Learning, Transformers, Sequence Models, Image Retrieval, Image Recognition, Classification, Entity Extraction, Recurrent Neural Networks (RNNs), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNNs), TensorBoard, Generative Adversarial Networks (GANs), Topic Modeling, Linux, Language Models, PostgreSQL, Recommendation Systems, Algorithms, Generative Pre-trained Transformer 3 (GPT-3), XGBoost, Data Extraction, Data Manipulation, Snowflake, Analytics, MySQL, Amazon Web Services (AWS), Handwriting Recognition, Data Analysis, Version Control Systems, Communication, Data Engineering, Regression, RAPIDS, APIs, Amazon SageMaker, NoSQL, Image Search, Amazon Rekognition, AI Design, Jupyter Notebook, Flask, NVIDIA TensorRT, Microservices Architecture, Data Scientist, Chatbots, Jupyter, Cloud, Anomaly Detection, OpenAI GPT-4 API, Chatbot Conversation Design, Integration, Programming, AI Programming, Bitbucket, Data Cleaning, Unstructured Data Analysis, Machine Learning Automation, Kubernetes, Amazon Elastic Container Service (ECS), Image Analysis, Large Language Models (LLMs), Project Management, Statistical Analysis, Data Reporting, Data Cleansing, Big Data, Databricks, Azure SQL Databases
ML Engineer
2019 - 2020
Trantor Software
- Designed and deployed an OCR solution that outperformed the accuracy of Google OCR by 12% as benchmarked on the ICDAR 2013 dataset. Reduced the overall cost from an estimated $1 million to less than $50,000 per year.
- Prototyped a natural language processing (NLP)--based bot using recurrent neural networks to parse comments on the website, assess sentiments, and classify them into themes. Enabled standard responses and activation of the needed systems to respond.
- Worked on a trained convolutional neural networks model using INT8 fixed-point arithmetic. Designed algorithms to decide the Q. Collaborated to enable classifiers, detectors, generative adversarial networks (GANs), and segmentation networks.
- Trained a software categorization and brand recognition tool using word2vec, N-grams embeddings, and kNN classifier. This tool was capable of matching product names and titles with descriptions.
- Managed quantization-aware training of convolutional neural networks and worked on fine-tuning and post-training quantization.
Technologies: Machine Learning, Deep Learning, Data Science, SQL, Artificial Intelligence (AI), Optical Character Recognition (OCR), Data Visualization, Optimization, Unsupervised Learning, Clustering, Code Review, Remote Team Leadership, Graphics Processing Unit (GPU), Transfer Learning, Transformers, Sequence Models, Image Recognition, Classification, Recurrent Neural Networks (RNNs), Siamese Neural Networks, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNNs), TensorBoard, Generative Adversarial Networks (GANs), Topic Modeling, Linux, Language Models, Python 3, Recommendation Systems, Algorithms, Generative Pre-trained Transformer 3 (GPT-3), XGBoost, Data Extraction, Data Manipulation, Snowflake, Analytics, MySQL, Handwriting Recognition, Google Vision API, Data Analysis, Version Control Systems, Communication, Data Engineering, Regression, RAPIDS, APIs, Amazon SageMaker, NoSQL, Image Search, Amazon Rekognition, AI Design, Jupyter Notebook, Flask, Microservices Architecture, Data Scientist, Chatbots, Jupyter, Cloud, Google Cloud Platform (GCP), Anomaly Detection, OpenAI GPT-4 API, Chatbot Conversation Design, Integration, Programming, AI Programming, Data Cleaning, Unstructured Data Analysis, Machine Learning Automation, Image Analysis, Large Language Models (LLMs), Project Management, Regex, Statistical Analysis, Data Reporting, Data Cleansing, You Only Look Once (YOLO), Elasticsearch, Azure SQL Databases
Software Engineer
2018 - 2019
Yamaha Motor Solutions
- Conceptualized and built a ConvLSTM model on TensorFlow for hand gesture recognition. Used Bayesian optimization techniques for hyperparameter tuning.
- Worked on single-shot face recognition using SSD MobileNet for detection and Inception-ResNet v2 for feature extraction. Introduced TP-GAN for photorealism and identity, preserving the frontal face-view synthesis from any pose.
- Led a team to propose and implement predictive analysis for retail sales forecasting using ARIMA, exponential smoothing, and Holt's Winter models. This resulted in a 5.35% increase in quarterly sales.
- Implemented conditional Wasserstein generative adversarial network (cWGANs) to synthesize new vehicle designs from given samples.
Technologies: Python 3, Machine Learning, Data Science, SQL, Artificial Intelligence (AI), Neural Networks, Data Visualization, Optimization, Code Review, Graphics Processing Unit (GPU), Transfer Learning, Machine Vision, Sequence Models, Classification, Siamese Neural Networks, Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANN), TensorBoard, Generative Adversarial Networks (GANs), Linux, Algorithms, XGBoost, Data Extraction, Data Manipulation, Snowflake, Analytics, MySQL, Handwriting Recognition, Data Analysis, Version Control Systems, Communication, Data Engineering, Regression, APIs, Amazon SageMaker, NoSQL, AI Design, Jupyter Notebook, Flask, Data Scientist, Jupyter, Cloud, Integration, Programming, AI Programming, Data Cleaning, Unstructured Data Analysis, Machine Learning Automation, Image Analysis, Project Management, Regex, Statistical Analysis, Data Reporting, Data Cleansing, You Only Look Once (YOLO), Point Cloud Data, Point Clouds, Azure SQL Databases
Experience
AI-based Document Capturing & Processing | AIDCAP
Fully automated the document capture and information extraction process using NLP and computer vision, which mainly dealt with invoices and orders from different sources, including emails, PDFs, DOCX, or images.
I also built and trained custom models for region proposal and text recognition with enhanced semantic understanding using Transformer.
I also built and trained custom models for region proposal and text recognition with enhanced semantic understanding using Transformer.
Search Relevance & Ranking
https://www.airtelxstream.in/Improved search relevance and ranking metrics like click-through rates (CTR), conversion rates, and ad revenue for organic and ad merchants at an eCommerce site.
1. Alternated search keyword generation with semantic search, increasing ad revenue by 20%. The system is scaled to handle 2,000+ requests per second using a Milvus-based vector search and is automatically refreshed daily.
2. I developed a custom deep semantic matching model trained to match search queries with products. I also worked on search query classification using custom-trained embeddings for query understanding to match the search query with product leaf categories.
3. Improved CTR by 50%, leading to a 37% incremental uplift in revenue and a 17% decrease in discount utilization. I improved the CTR using ML-based learn-to-rank models, starting from simple linear regression and tree-based models like LightGBM and XGBoost. These were combined with deep models for search-query understanding.
1. Alternated search keyword generation with semantic search, increasing ad revenue by 20%. The system is scaled to handle 2,000+ requests per second using a Milvus-based vector search and is automatically refreshed daily.
2. I developed a custom deep semantic matching model trained to match search queries with products. I also worked on search query classification using custom-trained embeddings for query understanding to match the search query with product leaf categories.
3. Improved CTR by 50%, leading to a 37% incremental uplift in revenue and a 17% decrease in discount utilization. I improved the CTR using ML-based learn-to-rank models, starting from simple linear regression and tree-based models like LightGBM and XGBoost. These were combined with deep models for search-query understanding.
Callup AI | Chatbot Platform
https://www.airtel.in/contact-usA generative chatbot for user queries using a Transformer-based Seq2Seq model from chat messages. It is currently deployed commercially, handling thousands of daily conversations in English or Hinglish with almost 85% accuracy.
The chatbot is also comprised of the nearest neighborhood-based intent detection solution, which uses pre-tagged customer messages to tag new customer messages for the support team. The idea was to reduce customer service workload by automatically replying to user messages with pre-defined responses based on intent detection.
The chatbot is also comprised of the nearest neighborhood-based intent detection solution, which uses pre-tagged customer messages to tag new customer messages for the support team. The idea was to reduce customer service workload by automatically replying to user messages with pre-defined responses based on intent detection.
Candidate Recommendation Engine
A tool to automate matching job descriptions with resumes, facilitating the process of getting top candidates out of thousands based on several parameters like experience, education, skillset, projects, and location.
By using FastText, NER, LayoutLM, Layout Parser, and a tree-based
embedding search algorithm, we were able to sift through thousands of resumes to find the best fit for the job. We also added new features like face search and real-time blacklisting.
Overall, we reduced manual effort by over 50%.
By using FastText, NER, LayoutLM, Layout Parser, and a tree-based
embedding search algorithm, we were able to sift through thousands of resumes to find the best fit for the job. We also added new features like face search and real-time blacklisting.
Overall, we reduced manual effort by over 50%.
Retail Sale Forecasting
https://www.yamaha-motor-india.com/Analyzed the sales trends and sizing curve distribution across categories and product lines to give recommendations on the localized sizing distribution for a demand forecast model. Then, I proposed and implemented predictive analysis for biweekly retail sales forecasting using ARIMA, exponential smoothing, and Holt-Winters methods.
Additionally, I incorporated Bayesian hyperparameter tuning with hybrid modeling using principal component analysis, support vector machines, and ensemble learning methods with Random Forests and XGBoost. This led to a 10% year-on-year increase in key metrics such as sales revenue.
Additionally, I incorporated Bayesian hyperparameter tuning with hybrid modeling using principal component analysis, support vector machines, and ensemble learning methods with Random Forests and XGBoost. This led to a 10% year-on-year increase in key metrics such as sales revenue.
Churn Prediction
Initiated customer churn prediction in a subscription-based purchase model. The first use case was for a telecommunications provider, and the second was for a retail giant, where I leveraged structured and unstructured sales and customer information, including chats, ratings, reviews, emails, and voice chat transcripts for designing KPIs. The model could predict churns almost two weeks before it happened.
I also planned and implemented an A/B test framework and SQL queries. The framework defined key metrics to analyze model performance statistical significance.
I also planned and implemented an A/B test framework and SQL queries. The framework defined key metrics to analyze model performance statistical significance.
Virtual Try-on
A real-time apparel-based virtual try-on tool. It uses a cutting-edge complex ML pipeline that provides a real-time, immersive, clothing-trial experience on a phone or kiosk with a single image and well-thought-out recommendations to help boost sales.
The tool increased in-store purchases by over 25% and via-app purchases by over 20%.
The tool increased in-store purchases by over 25% and via-app purchases by over 20%.
Face Dedupe Platform | Face Recognition, Matching, & Retrieval
https://www.airtel.in/bank/video-kycA platform with various microservices, including face recognition, face matching, and face liveness-check models.
I updated the platform with face segmentation, face blur detection, document forgery detection, real-time blacklisting, and face search to improve service efficiency and reduce manual effort by over 25%.
I updated the platform with face segmentation, face blur detection, document forgery detection, real-time blacklisting, and face search to improve service efficiency and reduce manual effort by over 25%.
Price Optimization
https://tiki.vn/Optimized retail product prices for Tiki using demand prediction by ML models and time-series historical sales data; LightGBM model is trained to predict sales at daily and 3-day frequencies.
The work included analyzing data, engineering features, managing stakeholders, designing A/B experiments, and performing statistical analysis for hypothesis testing.
The work included analyzing data, engineering features, managing stakeholders, designing A/B experiments, and performing statistical analysis for hypothesis testing.
Education
2014 - 2018
Bachelor's Degree in Computer Science
DIT University - Dehradun, India
Certifications
SEPTEMBER 2019 - PRESENT
Deep Learning Specialization
Coursera
MAY 2018 - PRESENT
Machine Learning
Coursera
APRIL 2018 - PRESENT
Big Data Modeling and Management Systems
Coursera
MARCH 2018 - PRESENT
Programming the Internet of Things (IoT) Specialization
Coursera
Skills
Libraries/APIs
TensorFlow, PyTorch, Python API, OpenCV, Scikit-learn, Pandas, NumPy, SciPy, Natural Language Toolkit (NLTK), SpaCy, XGBoost, RAPIDS, REST APIs, OpenAI Assistants API, Google Vision API, Keras, Amazon Rekognition, DeepSpeed, OpenAI API, DeepSpeech
Tools
TensorBoard, Amazon SageMaker, ChatGPT, Jupyter, Bitbucket, You Only Look Once (YOLO), Claude, OpenAI Gym, Amazon Elastic Container Service (ECS), Dialogflow, NGINX, GIS, CPLEX, Apache Airflow, AutoCAD, Zapier, GraphRAG, DeepSeek, AI Prompts
Languages
Python, SQL, Python 3, Regex, Bash, Snowflake, JavaScript, Bash Script
Frameworks
Flask, Accelerate
Paradigms
Siamese Neural Networks, ETL, Microservices Architecture, Automation, Anomaly Detection, Microservices
Platforms
Linux, Jupyter Notebook, Docker, Azure, Amazon Web Services (AWS), Google Cloud Platform (GCP), Kubernetes, Mixpanel, Azure AI Studio, Databricks, Blockchain, Voiceflow
Industry Expertise
Project Management
Storage
Azure SQL Databases, PostgreSQL, MySQL, NoSQL, Google Cloud, Elasticsearch
Other
Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, Software Deployment, Software Design, Statistics, Statistical Modeling, Data Analytics, Machine Learning Operations (MLOps), Image Processing, BERT, Long Short-term Memory (LSTM), Data Science, Predictive Modeling, Hugging Face, Artificial Intelligence (AI), Optical Character Recognition (OCR), Neural Networks, Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANN), Time Series Analysis, Entity Extraction, Classification, Image Recognition, Image Retrieval, Object Detection, Sequence Models, Machine Vision, Transformers, Transfer Learning, Graphics Processing Unit (GPU), Remote Team Leadership, Code Review, Generative Adversarial Networks (GANs), Clustering, Topic Modeling, Data Visualization, Mathematics, Optimization, Language Models, Recommendation Systems, Algorithms, Data Collection, Data Extraction, Data Manipulation, Large Data Sets, Analytics, Data Analysis, Models, Version Control Systems, Communication, Modeling, Data Engineering, Regression, APIs, Image Search, AI Design, Fine-tuning, Causal Inference, Data Scientist, Generative Pre-trained Transformers (GPT), Decision Trees, Chatbots, Cloud, OpenAI GPT-4 API, Chatbot Conversation Design, Integration, Programming, AI Programming, Data Cleaning, Unstructured Data Analysis, Machine Learning Automation, Language Learning, Image Analysis, Large Language Models (LLMs), Statistical Analysis, Data Reporting, Data Cleansing, API Integration, Prompt Engineering, Image Generation, Text to Image, Retrieval-augmented Generation (RAG), LangChain, FastAPI, Multi-agent Systems, Real-time Data, Gemini, AI Agents, Distributed Systems, Multivariate Statistical Modeling, GPU Computing, Big Data, Web Scraping, Unsupervised Learning, Generative Pre-trained Transformer 3 (GPT-3), A/B Testing, Revenue Optimization, Pricing Strategy, Handwriting Recognition, Stable Diffusion, NVIDIA TensorRT, Software Architecture, OpenAI GPT-3 API, Financial Modeling, Cognitive Behavioral Therapy (CBT), Data Structures, User Journeys, Customer Journeys, Amazon API Gateway, Generative Artificial Intelligence (GenAI), Point Cloud Data, Application Packaging, AI Modeling, Quantization, Vector Databases, Internet of Things (IoT), Industrial Internet of Things (IIoT), Computational Biology, Oncology & Cancer Treatment, Genomics, Molecular Biology, Biology, Bittensor, 3D, Diffusion Models, ControlNet, Data Strategy, Data, User Interface (UI), Airtable, Web Applications, Natural Language Understanding (NLU), Point Clouds, Multimodal GenAI, Integrated Development Environments (IDE), Text to Image AI, DALL-E, OpenAI, Mistral AI, Llama 2, Llama 3, Neural Network Pruning, Llama, FSDP, Parallelism, Amazon Bedrock, NVIDIA TensorRT-LLM, Graphics, Image to Video
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