Charles is available for hire
Hire CharlesCharles Camp
Verified Expert in Engineering
Artificial Intelligence (AI) Developer
Dijon, France
Toptal member since August 24, 2020
Bio
Charles is a senior AI developer with nine years of experience using AI technology, including ChatGPT and DeepSeek, and deploying machine learning models, particularly LLM applications over the last two years. He has experience working with large corporations, startups, and laboratories. He is also an AWS Certified Solution Architect and can deploy models creating APIs or containerized applications.
Portfolio
Christopher Rec
Artificial Intelligence (AI), Python, LangChain, Neo4j, Cypher...
Tavern Research Inc
LangChain, Artificial Intelligence (AI), Python, Pinecone, LlamaIndex, Flask
ITG AUTOMOTIVE LLC
Artificial Intelligence (AI), Natural Language Processing (NLP)...
Experience
- Artificial Intelligence (AI) - 9 years
- Pandas - 9 years
- Python - 9 years
- Flask - 6 years
- Google Cloud Platform (GCP) - 6 years
- LlamaIndex - 2 years
- LangChain - 2 years
- DeepSeek - 1 year
Availability
Part-time
Preferred Environment
Python, Amazon Web Services (AWS), Natural Language Processing (NLP), Time Series Analysis, Transformers, Reinforcement Learning, DeepSeek
The most amazing...
...model I've built can identify people with a connection to financial crimes.
Work Experience
Senior Software Engineer (via Toptal)
2024 - 2024
Christopher Rec
- Enhanced knowledge graph generation software. Improved the generation of knowledge graphs from text files and PDFs, optimizing the extraction of structured information for more accurate insights.
- Focused on client presentations and product promotion. Presented product updates and new features to clients, with a focus on driving engagement and adoption through clear communication and demonstrations.
- Carried out comprehensive testing for the improved UX. Conducted rigorous stress testing, SDK testing, and UI testing to identify and recommend enhancements, ensuring a smooth and intuitive user experience.
- Integrated cutting-edge tools. Seamlessly integrated advanced AI frameworks such as LlamaIndex and LangChain, expanding the software's capabilities in natural language processing and data retrieval.
- Documented use cases for promotion. Authored blog posts documenting use cases to support product promotion and educate potential users on its practical applications.
Technologies: Artificial Intelligence (AI), Python, LangChain, Neo4j, Cypher, Amazon Web Services (AWS)
AI LangChain Developer
2024 - 2024
Tavern Research Inc
- Developed various AI pipelines using LangChain to automate speech enhancement functionalities.
- Integrated AI pipelines with a Flask application, including designing and implementing DB models and UI components.
- Got an AI pipeline running in the background, leveraging Celery.
Technologies: LangChain, Artificial Intelligence (AI), Python, Pinecone, LlamaIndex, Flask
AI Engineer
2024 - 2024
ITG AUTOMOTIVE LLC
- Extracted key details from contracts using ChatGPT, LangChain and Pydantic.
- Benchmarked a hierarchical clustering algorithm to group extracted key terms based on their content and organize them.
- Integrated the solution and deployed it on AWS using Lambdas.
Technologies: Artificial Intelligence (AI), Natural Language Processing (NLP), OpenAI GPT-4 API, Generative Pre-trained Transformers (GPT), Amazon Web Services (AWS), Machine Learning Operations (MLOps), Azure, Machine Learning, LangChain, DeepSeek
AI LLM Expert
2023 - 2024
Huxley
- Built a chatbot trained on deaddiction program literature to help people get sober for different types of addictions, like alcohol, overeating, etc.
- Built the API of the chatbot and deployed it on GCP in a scalable manner using Docker and Kubernetes.
- Transcribed audio testimonials to enrich the quantity of data available for the chatbot's recommendations.
- Used llama-index to improve the indexing of the vector store (embeddings of the documents) and get better-quality answers from the chatbot.
- Built a model to extract quotes and stories from documents to use them as testimonial summaries.
- Trained a recommender system to suggest inspiring quotes to the users based on their profiles. This included experience in the program, past likes/dislikes, inventories, and app activity.
- Produced documentation and had a live session with the founder to explain the technicalities of the solutions.
- Trained a reinforcement learning model to shorten the path to sobriety. Used this model to choose what actions the sponsor should take to make the user evolve as fast and reliably as possible in the program and remain sober.
Technologies: Artificial Intelligence (AI), Generative Pre-trained Transformers (GPT), OpenAI GPT-3 API, OpenAI GPT-4 API, LangChain, Llama 2, LlamaIndex, Large Language Models (LLMs), Reinforcement Learning, Recommendation Systems, Docker, Kubernetes, Google Cloud Platform (GCP), FastAPI
AI Developer
2023 - 2023
Non-Fungible Films, Inc.
- Fine-tuned stable diffusion models to be usable with the company's fictive characters.
- Deployed a Discord bot similar to Midjourney but using the custom stable diffusion model.
- Integrated the model with a stable diffusion UI to enable inpainting, image to image, and other applications.
Technologies: Python, Artificial Intelligence (AI), Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Computer Vision, Machine Learning, Node.js
ML Engineer
2022 - 2023
Global CPG Company
- Created a pipeline to automate look-alike audience computations leveraging internal consumer behavior data.
- Compared models to achieve the highest performances and hyperparameters tuning.
- Created custom PySpark and scikit-learn estimators to integrate with PySpark and scikit-learn pipelines, respectively.
Technologies: Python, Machine Learning, PySpark, Scikit-learn, Pandas
ML and NLP Engineer
2021 - 2022
Phragmites, Inc.
- Set up an EC2 server, analyzed Telegram messages stored on a Postgres DB, and classified them as relevant or not for a given crypto-related project.
- Built a bot message-detection model using near-duplicates clustering approaches.
- Quantified the influence of Telegram users in crypto-centered conversations using graph theory.
- Trained a NER model to detect crypto project names in Telegram messages.
Technologies: Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Machine Learning, Trend Analysis, Sentiment Analysis, Cryptocurrency, Artificial Intelligence (AI), Google Cloud, Python, SQL, Pandas, Scikit-learn, Data Science, Natural Language Toolkit (NLTK), SpaCy, Hugging Face, Neural Networks, Communication, Elasticsearch
Senior Data Scientist
2021 - 2022
Trust & Safety Laboratory
- Trained machine learning models to find controversial topics in tweets. Controversial topics were defined as potentially containing harmful misinformation.
- Trained ML models to detect false claims and misinformation in tweets.
- Built a pipeline to collect human loop reviews (AWS), automated the labeling of potentially misleading tweets, and performed website scraping.
- Developed a serverless framework to automatize social media screening tasks.
Technologies: Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Docker, Kubernetes, Bazel, Machine Learning, Python, Scikit-learn, Pandas, Data Science, Linux, Amazon Web Services (AWS), Neural Networks, SpaCy, Artificial Intelligence (AI), Sentiment Analysis, Test Automation, Hugging Face
Python Developer | AI
2020 - 2021
Click Factura SA de CV
- Transcribed and summarized Spanish audio meetings: fine-tuned text-to-speech models ( DeepSpeech, NeMo, and Wav2Vec) and used text summary and diarisation models.
- Trained an OCR model to extract the information on Mexican expense tickets.
- Integrated the models into the existing Django application by creating APIs.
- Deployed the models using Docker containers and Flask.
Technologies: Python, Machine Learning, TensorFlow, Test Automation, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Artificial Intelligence (AI), Amazon Web Services (AWS), Kubernetes, Docker, Django, APIs, Optical Character Recognition (OCR), Speech to Text, SQL, Data Science, Linux, Neural Networks, Communication, Google Cloud, Hugging Face
Machine Learning Expert | Digital Advertisement
2020 - 2020
Primal Analytics
- Deployed Lambda to detect anomalies in Google ads statistics automatically.
- Compared various state-of-the-art ML models for time-series anomaly detection.
- Set up the AWS account for data storage and Lambda execution.
Technologies: Machine Learning, Digital Advertising, Python, Time Series Analysis, Scikit-learn, Anomaly Detection, SQL, Pandas, Data Science, Amazon Web Services (AWS), Neural Networks, ARIMA, Artificial Intelligence (AI), Trend Analysis
Senior Data Scientist
2019 - 2020
Glovo
- Designed, implemented, and deployed a customer lifetime value model. This was deployed on an EC2 instance using Luigi and scheduled with Jenkins.
- Used linear programming to optimize pickers' time shifts.
- Built an end-to-end pipeline to decide whether or not to show a product in the app based on its probability of being available in the store to improve the customer experience. The model was trained on SageMaker and then deployed on an EC2 instance.
Technologies: Linear Programming, TensorFlow, Data Science, Pandas, Machine Learning, Scikit-learn, Python, XGBoost, Redshift, Amazon SageMaker, Amazon Web Services (AWS), Luigi, SQL, Communication, Artificial Intelligence (AI), Anomaly Detection
Data Scientist
2016 - 2019
Credit Suisse
- Designed and deployed machine learning models to detect money laundering using transaction data.
- Led the Negative News screening project to automatically screen news data and find associations with financial crimes to enrich the risk scoring model.
- Used NLP to measure the impact of news data on the sales of financial products.
- Organized data sourcing and mapping of various transaction and KYC data sources on a big data platform. Also handled the design and implementation of a data model for the transaction and KYC data to facilitate transaction monitoring.
Technologies: Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), TensorFlow, PySpark, Data Science, Pandas, Machine Learning, Scikit-learn, Python, SpaCy, SQL, Artificial Intelligence (AI), Communication, Natural Language Toolkit (NLTK), R, Time Series Analysis, ARIMA, XGBoost, Sentiment Analysis, Anomaly Detection, Hugging Face, Elasticsearch, Test Automation
Research Scholar
2016 - 2016
Carnegie Mellon University
- Designed and implemented a model to predict the survival of patients after a cardiac arrest using their brain activity data (multivariate time series).
- Built an evaluation to give a better score to models predicting the survival earlier.
- Clustered patients to identify common characteristics and deduce specific preventive actions to increase their survival rate.
Technologies: ARIMA, Time Series Analysis, R, Data Science, Pandas, Machine Learning, Scikit-learn, Linux, Artificial Intelligence (AI), Trend Analysis, Anomaly Detection
Data Scientist Intern
2015 - 2015
Capgemini
- Set up a Spark cluster to read sensor data from HDFS and preprocess it.
- Built a scalable supervised model to detect manufacturing breakdowns using multivariate time series data (sensor data).
- Fine-tuned and validated the model. Identified main features leading to breakdowns.
Technologies: ARIMA, Time Series Analysis, PySpark, Data Science, Pandas, Machine Learning, Scikit-learn, Python, Linux, XGBoost, Artificial Intelligence (AI), Anomaly Detection
Experience
Recommender System
We are provided a matrix V of dimensions (number of users, number of movies). This matrix is filled with 0 if the user did not like the movie and one if the user liked it. The rest of the values are NaNs.
In the first step, we use non-negative matrix factorization (NMF) to find two matrices W and H of respective sizes (number of users, K) and (K, number of movies) that minimize the difference between V and WH where K is a small value (< 10). That means we look for W and H such as WH is close to V.
Afterward, we use W and H to cluster the users and can now recommend movies that will be liked by their assigned cluster.
In the first step, we use non-negative matrix factorization (NMF) to find two matrices W and H of respective sizes (number of users, K) and (K, number of movies) that minimize the difference between V and WH where K is a small value (< 10). That means we look for W and H such as WH is close to V.
Afterward, we use W and H to cluster the users and can now recommend movies that will be liked by their assigned cluster.
Face and Image Recognition
A facial recognizer to detect faces using a webcam and OpenCV. This project was first extended to simple objects before being extended again to custom objects retraining a pre-trained state-of-the-art TensorFlow model.
Education
2014 - 2016
Master's Degree in Data Science
Grenoble Institute of Technology - Grenoble, France
2011 - 2014
Bachelor's Degree in Computer Science
Grenoble Institute of Technology - Grenoble, France
Certifications
JULY 2023 - PRESENT
Generative AI with Large Language Models
Coursera
APRIL 2022 - PRESENT
Decentralized Finance (DeFi)
Coursera
FEBRUARY 2022 - FEBRUARY 2025
AWS Solutions Architect Associate
Pearson VUE
DECEMBER 2020 - PRESENT
Django for Everybody
University of Michigan | via Coursera
Skills
Libraries/APIs
Scikit-learn, Pandas, PySpark, SpaCy, Natural Language Toolkit (NLTK), XGBoost, TensorFlow, Luigi, OpenCV, Node.js
Tools
Amazon SageMaker, ARIMA, Bazel, DeepSeek
Languages
Python, SQL, R, Cypher
Frameworks
LlamaIndex, Flask, Django
Paradigms
Anomaly Detection, Linear Programming, Test Automation
Platforms
Linux, Amazon Web Services (AWS), Docker, Kubernetes, Google Cloud Platform (GCP), Azure
Storage
Neo4j, Redshift, Google Cloud, Elasticsearch
Other
Time Series Analysis, Natural Language Processing (NLP), Machine Learning, Data Science, Artificial Intelligence (AI), Communication, Sentiment Analysis, Hugging Face, Generative Pre-trained Transformers (GPT), PEFT, LoRa, Transformers, OpenAI GPT-3 API, OpenAI GPT-4 API, LangChain, Neural Networks, APIs, Speech to Text, Trend Analysis, Cryptocurrency, Reinforcement Learning, Llama 2, Recommendation Systems, FastAPI, Analysis of Variance (ANOVA), Optical Character Recognition (OCR), Decentralized Finance (DeFi), Digital Advertising, Computer Vision, Large Language Models (LLMs), Machine Learning Operations (MLOps), Pinecone
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