Ricardo is available for hire
Hire RicardoRicardo G. Sousa
Verified Expert in Engineering
Artificial Intelligence (AI) Developer
Porto, Portugal
Toptal member since May 13, 2024
Bio
Ricardo is an experienced AI and product strategy expert and a major eCommerce platform lead. He has created advanced automations and AI personalization, recommendation, and search models, generating an annual revenue uplift of over $16 million. With 10+ years of experience, Ricardo excels at driving the development and successful launch of innovative AI platforms at scale, from early-stage startups to mature enterprises, translating data-driven insights into strategic competitive leverage.
Portfolio
Visor
AWS CLI, Python, PyTorch, Docker, Whisper...
Farfetch
Python, PyTorch, PySpark, Databricks, Docker, CircleCI, Sphinx, C4 Model...
Farfetch
Python, Information Retrieval, Machine Learning, Artificial Intelligence (AI)...
Experience
- Python - 15 years
- Strategy - 15 years
- Artificial Intelligence (AI) - 15 years
- Machine Learning - 15 years
- Computer Vision - 10 years
- Data Engineering - 8 years
- Vector Search - 8 years
- Semantic Search - 8 years
Availability
Part-time
Preferred Environment
Python, Artificial Intelligence (AI), Machine Learning, Strategy, Natural Language Processing (NLP), Vector Search, Conversational AI, Retrieval-augmented Generation (RAG), Data Engineering
The most amazing...
...thing I've built is a multimodal AI agent platform that blends computer vision and NLP, enhancing online shopping and increasing customer satisfaction by 30%.
Work Experience
Principal AI Engineer
2023 - 2024
Visor
- Designed, built, and deployed a scalable AI platform using data augmentation and transformers like large language models (LLMs) and BERT, improving customer service email categorization by 40% and enabling rapid AI model integration and deployment.
- Headed a top-performing AI division, driving innovation in customer service call transcription and report generation with GenAI for a responsible AI audit solution and ensuring timely product delivery.
- Mentored a remote team, resulting in a 75% reduction in development cycles.
- Leveraged XP/Lean startup methodologies and orchestrated high-impact initiatives by designing robust frameworks for testing, learning, and quality assurance to assess return on investment (ROI).
- Leveraged analytics and data-driven insights to shape product reformulations.
Technologies: AWS CLI, Python, PyTorch, Docker, Whisper, Generative Pre-trained Transformer 4 (GPT-4), OpenAI, DVC, Sphinx, C4 Model, PlantUML, LaTeX, Light LLMs, Large Language Model Operations (LLMOps), Large Language Models (LLMs), Data Augmentation, Natural Language Processing (NLP), Strategy, Communication, Lean Project Management, Remote Team Leadership, Git, Bash Script, Makefile, Transformer Models, FastAPI, Management, Team Leadership, Artificial Intelligence (AI), Machine Learning, Deep Learning, Bash, Jira, PyCharm, Data Engineering, Machine Learning Operations (MLOps), Docker Compose, Discord, Amazon Web Services (AWS), Scalable Web Services, Test-driven Development (TDD), APIs, OpenAI GPT-3 API, Generative Pre-trained Transformers (GPT), REST, API Integration, Hugging Face, Generative Artificial Intelligence (GenAI), Chatbots, Software Architecture, Data Science, Scikit-learn, OpenAI GPT-4 API, Fine-tuning, Embeddings from Language Models (ELMo), Semantic Search, Technical Leadership, Startups
Principal Data Scientist
2021 - 2023
Farfetch
- Improved a PySpark ETL process on Databricks and a LightGBM re-ranker model to personalize the customer experience and product recommendations. Analyzed customer behaviors, such as lifetime value (LTV), for insight discovery.
- Headed a $2.5 million project to create multimodal AI agents, achieving a 30% uplift in customer positive responses.
- Built an ecosystem, resulting in new business knowledge, architecture for retrieval-augmented generation (RAG), and new AI models incorporating natural language processing (NLP) with LLMs.
- Designed and implemented A/B tests and experiments to evaluate personalization strategies' impact on key performance indicators (KPIs) like add-to-cart rates.
- Partnered product management and senior leadership with actionable insights and strategic recommendations.
Technologies: Python, PyTorch, PySpark, Databricks, Docker, CircleCI, Sphinx, C4 Model, PlantUML, Elasticsearch, LaTeX, Google Cloud Platform (GCP), BigQuery, Data Augmentation, Vector Search, Information Retrieval, Artificial Intelligence (AI), Machine Learning, Natural Language Processing (NLP), Natural Language Generation (NLG), GitHub, GitHub Actions, Flask, Personalization, Recommendation Systems, Learning to Rank, Jira, Git, MLflow, Communication, Strategy, Remote Team Leadership, Lean Project Management, Linux, Complex Problem Solving, Creative Problem Solving, Large Language Model Operations (LLMOps), Large Language Models (LLMs), Makefile, Deep Learning, Team Leadership, Management, Conversational AI, Bash, Bash Script, Search, PyCharm, Data Engineering, Retrieval-augmented Generation (RAG), Open-source LLMs, Machine Learning Operations (MLOps), Docker Compose, Zoom, Discord, Scalable Web Services, Test-driven Development (TDD), APIs, OpenAI GPT-3 API, Generative Pre-trained Transformers (GPT), REST, API Integration, Hugging Face, Generative Artificial Intelligence (GenAI), Chatbots, Software Architecture, Data Science, Scikit-learn, OpenAI GPT-4 API, Fine-tuning, Embeddings from Language Models (ELMo), SQL, Semantic Search, eCommerce, Technical Leadership
Data Science Manager
2019 - 2021
Farfetch
- Leveraged novel approaches to optimize user experience, including customized segmentation and faceted search enhancements. Proposed impactful A/B tests that delivered an outstanding $14 to $16 million increase in potential prospects.
- Secured $2.4 million in collaborative funding from the Portuguese government to pioneer the development of the inaugural multimodal conversational AI agent.
- Uncovered insights in clickstream data using LightGBM models to enhance relevance in normalized discounted cumulative gain (nDCG) while aligning with business goals through personalized recommendations and faceted search.
Technologies: Python, Information Retrieval, Machine Learning, Artificial Intelligence (AI), Elasticsearch, PT-Ranking, Docker, C4 Model, PlantUML, Google Cloud Platform (GCP), BigQuery, Jenkins, Octopus Deploy, Deep Learning, Clustering, Bash, Team Leadership, Management, Conversational AI, Complex Problem Solving, Creative Problem Solving, Strategy, Makefile, Personalization, Bash Script, Search, Lean Project Management, Jira, PyCharm, Data Engineering, Machine Learning Operations (MLOps), Docker Compose, Zoom, Discord, Scalable Web Services, Test-driven Development (TDD), APIs, REST, API Integration, Chatbots, Software Architecture, Data Science, Scikit-learn, Fine-tuning, SQL, Semantic Search, eCommerce, Technical Leadership
Data Scientist
2015 - 2019
Farfetch
- Led two teams and managed eight data scientists, conducting 15 A/B tests on Farfetch's listing pages. This resulted in search enhancements that drove a projected annual revenue increase from $6.2 to $8.7 million, with a 60% overall success rate.
- Secured the first chatbot team. Introduced a new service leveraging NLP pipelines and techniques such as tokenization, stemming, lemmatization, and word embeddings for ticket categorization, i.e., XGBoost, reducing average reply time by 28%.
- Fostered university partnerships, improving the DYM methodology with a projected annual gross merchandise value (GMV) uplift of $0.4 million and automated product categorization.
- Headed A/B testing on listing pages, estimating $620 thousand to $6.2 million in annual revenue gains. Implemented AI/ML solutions using Python for N-gram-based and point-wise faceted searches.
- Disrupted the platform with a novel functionality to visually search in the product catalog, contributing significantly to customer engagement in the marketplace.
Technologies: Python, Elasticsearch, Docker, Artificial Intelligence (AI), Machine Learning, Learning to Rank, Jenkins, Octopus Deploy, Computer Vision, Information Retrieval, BigQuery, Vector Search, Makefile, Team Leadership, Management, Bash, Bash Script, Search, Personalization, Jira, PyCharm, Data Engineering, Machine Learning Operations (MLOps), Docker Compose, Scalable Web Services, API Integration, Chatbots, Software Architecture, Data Science, Scikit-learn, Fine-tuning, SQL, Semantic Search, eCommerce, Technical Leadership, Startups
Experience
Video Shorts Generator
https://github.com/rjgsousa/video-shorts-generatorThe Video Shorts Generator (VSG) is designed to help businesses enhance their online presence by streamlining video and blog content creation. The objective was to build a system from scratch within one week that could generate clips from long video recordings, enabling lead generation on brand marketplaces and improving productivity.
Integrating NLP, video analysis, and generative AI, VSG simplifies video creation while delivering relevant shorts. Users can swiftly create Instagram-ready clips while eliminating extensive video editing. Additionally, VSG generates blog articles based on extracted content.
My contributions include:
• Developing an end-to-end ecosystem and platform, resulting in new business knowledge and processes.
• Architecting RAG and GenAI components for seamless language model integration.
• Leveraging theme segmentation techniques for efficient, relevant clip generation.
• Implementing automations throughout the platform to streamline content creation and curation workflows.
The successful deployment of VSG has enabled the client to streamline content creation, increasing efficiency, cost savings, and audience engagement.
Integrating NLP, video analysis, and generative AI, VSG simplifies video creation while delivering relevant shorts. Users can swiftly create Instagram-ready clips while eliminating extensive video editing. Additionally, VSG generates blog articles based on extracted content.
My contributions include:
• Developing an end-to-end ecosystem and platform, resulting in new business knowledge and processes.
• Architecting RAG and GenAI components for seamless language model integration.
• Leveraging theme segmentation techniques for efficient, relevant clip generation.
• Implementing automations throughout the platform to streamline content creation and curation workflows.
The successful deployment of VSG has enabled the client to streamline content creation, increasing efficiency, cost savings, and audience engagement.
Face Liveness and Age Detector
https://github.com/rjgsousa/face-liveness-age-detectorThe Face Liveness and Age Detector (FLAD) is a computer vision system that enhances security and authentication by detecting and analyzing faces in images and videos. It can be incorporated into a security platform to determine the authenticity of detected faces, i.e., liveness detection, and estimate the age of individuals.
It addresses the growing need for robust facial recognition and authentication solutions, particularly in industries where identity verification is critical, such as financial services or governmental institutions.
My contributions were on:
• The architecture of the system's liveness detection module, leveraging cutting-edge deep learning techniques to accurately differentiate between genuine and spoofed faces.
• The optimization of the system's performance to ensure efficient and scalable deployment.
• The development of user-friendly APIs to seamlessly integrate the FLAD into existing systems and workflows.
The successful deployment of the FLAD can enable organizations to enhance their security. By accurately detecting spoofed faces and estimating age, the FLAD has proven invaluable in safeguarding sensitive information and ensuring the integrity of authentication processes.
It addresses the growing need for robust facial recognition and authentication solutions, particularly in industries where identity verification is critical, such as financial services or governmental institutions.
My contributions were on:
• The architecture of the system's liveness detection module, leveraging cutting-edge deep learning techniques to accurately differentiate between genuine and spoofed faces.
• The optimization of the system's performance to ensure efficient and scalable deployment.
• The development of user-friendly APIs to seamlessly integrate the FLAD into existing systems and workflows.
The successful deployment of the FLAD can enable organizations to enhance their security. By accurately detecting spoofed faces and estimating age, the FLAD has proven invaluable in safeguarding sensitive information and ensuring the integrity of authentication processes.
Multimodal Conversational AI Agents
https://farfetch-chat-rd.github.io/Developed a multimodal conversational AI agent for the online high-end fashion marketplace Farfetch. By mimicking a fashion expert, it creates a seamless, human-like conversational experience and aims to:
• Scale Farfetch's business while maintaining customer satisfaction.
• Leverage conversational AI to improve conversion rates.
• Leverage data and knowledge to provide personalized fashion recommendations.
• Revolutionize online shopping in high fashion with a cutting-edge conversational agent.
I've led this $2.5 million project, developed the ecosystem and platform, and achieved a 30% uplift in positive customer responses. My contributions include establishing new business knowledge, architecture for RAG, novel AI models like dialog state tracking, natural language understanding (NLU) with large language models such as JointBERT, and synthetic dataset generation procedures.
The project has tackled challenges like tracking evolving shopping needs through conversation and relating product details to dialogues for better engagement. The successful development has enabled Farfetch to deliver a high-touch, personalized experience at scale, meeting modern consumers' expectations and reinforcing its market leadership.
• Scale Farfetch's business while maintaining customer satisfaction.
• Leverage conversational AI to improve conversion rates.
• Leverage data and knowledge to provide personalized fashion recommendations.
• Revolutionize online shopping in high fashion with a cutting-edge conversational agent.
I've led this $2.5 million project, developed the ecosystem and platform, and achieved a 30% uplift in positive customer responses. My contributions include establishing new business knowledge, architecture for RAG, novel AI models like dialog state tracking, natural language understanding (NLU) with large language models such as JointBERT, and synthetic dataset generation procedures.
The project has tackled challenges like tracking evolving shopping needs through conversation and relating product details to dialogues for better engagement. The successful development has enabled Farfetch to deliver a high-touch, personalized experience at scale, meeting modern consumers' expectations and reinforcing its market leadership.
Education
2008 - 2012
PhD in Mathematics and Computer Science
University of Porto - Porto, Portugal
2007 - 2008
Master's Degree in Informatics and Applied Mathematics
University of Porto - Porto, Portugal
Skills
Libraries/APIs
Scikit-learn, PyTorch, PySpark
Tools
PyCharm, Jira, LaTeX, CircleCI, GitHub, BigQuery, Zoom, AWS CLI, Whisper, Jenkins, Git, Makefile, Docker Compose
Languages
Python, Bash, Bash Script, SQL
Platforms
Linux, Docker, Databricks, Google Cloud Platform (GCP), Amazon Web Services (AWS)
Storage
Elasticsearch
Paradigms
Test-driven Development (TDD), REST, Management
Frameworks
C4 Model, Flask
Other
Artificial Intelligence (AI), Machine Learning, Computer Vision, DVC, Light LLMs, Large Language Model Operations (LLMOps), Large Language Models (LLMs), Data Augmentation, Recommendation Systems, Search, Information Retrieval, Learning to Rank, Deep Learning, Team Leadership, Conversational AI, Complex Problem Solving, Creative Problem Solving, Strategy, Lean Project Management, Open-source LLMs, Machine Learning Operations (MLOps), Scalable Web Services, OpenAI GPT-3 API, API Integration, Chatbots, Software Architecture, Data Science, OpenAI GPT-4 API, Fine-tuning, Embeddings from Language Models (ELMo), Semantic Search, eCommerce, Technical Leadership, Vector Search, Clustering, Natural Language Generation (NLG), Retrieval-augmented Generation (RAG), APIs, Generative Pre-trained Transformers (GPT), Hugging Face, Generative Artificial Intelligence (GenAI), Startups, Discord, GitHub Actions, Optimization, Generative Pre-trained Transformer 4 (GPT-4), OpenAI, Sphinx, PlantUML, Transformer Models, PT-Ranking, Octopus Deploy, Natural Language Processing (NLP), Communication, Remote Team Leadership, FastAPI, Personalization, MLflow, Data Engineering, Computer Vision Algorithms, Statistics, Bayesian Statistics, Naive Bayes, Bayesian Inference & Modeling, Algorithms, Stochastic Process, Numerical Methods, Numerical Analysis, Applications, Linear Optimization
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