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Vasco – PyTorch, Python, OpenAI, experts in Lemon.io

Vasco

From Germany (UTC+2)

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AI EngineerSenior
Data Scientist
9 years of commercial experience
Adtech
AI
Data analytics
Fintech
Machine learning
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Vasco – PyTorch, Python, OpenAI

Vasco is a skilled AI engineer with 8 years of experience tackling complex challenges across AI and data science. He has hands-on expertise with multi-modal LLMs, transformer architectures, and model quantization. Vasco’s work spans from building end-to-end AI solutions on AWS using services like AKS and Kubeflow to designing data models with LookML and dbt. His ability to create robust AI systems and drive impactful projects makes him an exceptional asset for any AI-focused initiative. On top of his technical skills, Vasco is known for his clear communication, problem-solving mindset, and collaborative approach, making him a valuable team player in any setting.

Main technologies
PyTorch
2.5 years
Python
7.5 years
OpenAI
2.5 years
AWS
7 years
AI
7 years
LLM
2 years
Additional skills
GCP
Kubernetes
Airflow
Data Science
Databricks
Data Modeling
AWS SageMaker
Amazon S3
Big Data
Data analysis
MLOps
Docker
Deep Learning
Vector Databases
Keras
Pinecone
Tensorflow
Hugging Face
NLP
LangChain
FastAPI
PostgreSQL
ETL
Direct hire
Potentially possible
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Experience Highlights

Senior Data Scientist
Nov 2024 - Ongoing6 months
Project Overview

An internationally leading technology enterprise operating in the optics and optoelectronics industries. Vasco has worked on three projects:

  • Time Series Forecasting (Python, Generative AI, Time Series)
  • Manufacturing Process Optimization (Python, Causal Discovery, Causal Inference)
  • Log Analytics Anomaly Detection (Python, Generative AI, Time Series)
Responsibilities:
  • Led time series forecasting initiatives across diverse departments, acting as the lead technical stakeholder and driving the adoption of advanced forecasting methodologies, including Generative AI-based approaches to enhance prediction accuracy and scenario modeling for financial forecasting.
  • Led and mentored a team of data scientists and machine learning engineers through the full lifecycle of machine learning products.
  • Functioned as a solutions architect for inter-departmental ETL and data architecture initiatives.
  • Led application of advanced causal discovery (PC/FCI, GES, LiNGAM) on high-dimensional sensor data, uncovering latent defect structures.
  • Architected and implemented doubly robust machine learning solutions (DML, DR-Learners) for unbiased causal eOect estimation of manufacturing parameters on lens scrape rate (MTF, defects).
  • Developed and evangelized the use of Bayesian Networks derived from causal DAGs for 'what-if' intervention simulations (Do-calculus), enabling proactive quality control strategies adopted by product teams.
  • Pioneered and scaled a reusable causal inference pipeline (integrating DoWhy/EconML) for automated ATE/CATE estimation.
  • Architected and led the development of an enterprise-scale log analytics platform, leveraging advanced unsupervised learning (e.g., autoencoders, isolation forests, clustering) for real-time, multi-source anomaly detection, significantly reducing Mean Time to Detection (MTTD) by 4%.
  • Led the integration of NLP techniques (e.g., log parsing, embeddings, topic modeling) with deep learning models (e.g., LSTMs, Transformers) to uncover nuanced anomalies in unstructured log data.
  • Engineered a modular, A/B testable framework for deploying and evaluating diverse anomaly detection algorithms (statistical, ML-based) on high-velocity streaming logs, enabling proactive alerting and automated incident correlation which reduced alert fatigue by 7%.
  • Orchestrated the design and deployment of a robust MLOps pipeline.
Project Tech stack:
Python
Machine learning
AI
Azure DevOps
Databricks
ETL
MLOps
Lead Data Scientist
Apr 2024 - Oct 20246 months
Project Overview

A digital advertising platform focused on optimizing campaign performance and maximizing ROI. It leveraged advanced algorithms and data-driven insights to deliver targeted ads across multiple channels. It helped businesses achieve greater reach, engagement, and efficiency in their advertising efforts.

Responsibilities:

Vasco was responsible for the following:

  • ideated and developed AI-driven product solutions tailored to client needs, leveraging LangChain and LlamaIndex for LLM application development and knowledge integration;
  • designed and deployed Retrieval Augmented Generation (RAG) pipelines to improve information retrieval and contextual responses, using Pinecone and Weaviate for vector search optimization;
  • built and fine-tuned Large Language Models (LLMs) for chatbot applications, utilizing OpenAI APIs, prompt engineering, and generative AI frameworks;
  • created scalable RESTful APIs with FastAPI to deploy AI and ML services, ensuring seamless integration with existing systems;
  • deployed generative AI models in production, focusing on quantization, inference optimization, and deployment using PyTorch, TensorFlow, and Hugging Face Transformers;
  • applied computer vision techniques for creative tagging and performance analysis, leveraging CLIP, Deep Learning, and Keras, driving campaign ROI improvements;
  • designed and implemented MLOps workflows, including monitoring, versioning, and scaling AI systems with Kubernetes, Docker, and cloud platforms like AWS EKS, GCP, and Azure;
  • developed robust data pipelines for real-time analytics and machine learning workflows, integrating BigQuery, DBT, and Databricks to enhance performance and scalability;
  • integrated vector databases (e.g., Pinecone, Weaviate) and embeddings for advanced semantic search and retrieval in AI-driven applications.
Project Tech stack:
Python
PyTorch
LangChain
LLM
OpenAI API
REST API
FastAPI
Tensorflow
Hugging Face
Keras
Deep Learning
AWS
GCP
Microsoft Azure
BigQuery
Databricks
Pinecone
WebRTC
Lead Data Scientist
Jan 2021 - Aug 20243 years 6 months
Project Overview

A solution that automates the generation of AdWords text and optimizes bidding strategies for recruitment campaigns. Designed to enhance performance, it streamlines ad creation while ensuring cost-effective delivery to the right audience. This tool helps employers maximize their recruitment ROI with minimal effort.

Responsibilities:

Vasco's main responsibilities included:

  • developed and architected very complex real-time ML systems multiple times to production as a team;
  • developed and managed an automated bidding model optimized to maximize revenue/margin, generating a 6% uplift in spend and a decrease in CPA;
  • developed and managed an automated text generation models (keywords, ad copy) using classic NLP and LLM techniques (self-hosting and external, prompt engineering, persuasion techniques);
  • addressed third-party cookie deprecation by using probabilistic clustering in full-funnel attribution, using Spark to pipeline data to enhance conversion tracking and data accuracy;
  • leveraged causal inference (double robust learners — econml) to refine conversion tracking, boosting accuracy and stakeholder confidence on top of clustering output;
  • developed, A/B tested, and deployed with Docker/Kubernetes a linear regression-based algorithm, achieving a 3.7% YoY revenue increase and enhancing pacing KPIs by 35%;
  • successfully led A/B testing for new product features, driving improvement in core company KPIs;
  • collaborated with cross-functional teams to enable data-driven decision-making and stakeholder buy-in; recruited elements for the DS team.
Project Tech stack:
AI
Data Science
NLP
LLM
Apache Spark
Docker
Kubernetes
Lead Data Scientist
Dec 2020 - Jul 20243 years 6 months
Project Overview

A solution that automated the generation of AdWords text and optimized bidding strategies for recruitment campaigns. Designed to enhance performance, it streamlined ad creation while ensuring cost-effective delivery to the right audience. This tool helped employers maximize their recruitment ROI with minimal effort.

Responsibilities:

Vasco carried out the following responsibilities:

  • created scalable RESTful APIs with FastAPI to deploy AI and ML services, ensuring seamless integration with existing systems;
  • deployed generative AI models in production, focusing on quantization, inference optimization, and deployment using PyTorch, TensorFlow, and Hugging Face Transformers;
  • applied computer vision techniques for creative tagging and performance analysis, leveraging CLIP, Deep Learning, and Keras, driving campaign ROI improvements;
  • designed and implemented MLOps workflows, including monitoring, versioning, and scaling AI systems with Kubernetes, Docker, and cloud platforms like AWS EKS, GCP, and Azure;
  • developed robust data pipelines for real-time analytics and machine learning workflows, integrating BigQuery, DBT, and Databricks to enhance performance and scalability;
  • integrated vector databases (e.g., Pinecone, Weaviate) and embeddings for advanced semantic search and retrieval in AI- driven applications.
Project Tech stack:
Python
PyTorch
Tensorflow
Hugging Face
REST API
Deep Learning
Keras
MLOps
AWS
GCP
Microsoft Azure
Kubernetes
Docker
BigQuery
Databricks
Pinecone
Vector Databases
AI
Data Scientist
May 2020 - Dec 20207 months
Project Overview

A technology-driven platform offered advanced solutions for data analysis, visualization, and decision-making. It combined cutting-edge tools with intuitive interfaces to empower businesses to transform complex data into actionable insights. The platform supported a wide range of industries, enabling smarter strategies and enhanced outcomes.

Responsibilities:

Among Vasco's contributions, there were the following:

  • developed a system to optimize portfolio risk: key risk KPIs (implied volatility, maximum drawdown) decreased on average 7.6%;
  • delivered a system for option pricing using deep learning, leading to an improvement in average daily returns of 2.7%;
  • managed end-to-end data processing, enhancing system performance through effective sourcing, preprocessing, and partitioning for model training and inference;
  • utilized NLP techniques to analyze tweets, creating a feature store (embedding) for machine learning models that enhanced stock market understanding;
  • developed a back-and-forward testing framework.
Project Tech stack:
Python
NumPy
Pandas
NLP

Education

2016
Machine Learning & Robots
MSc

Languages

Portuguese
Advanced
Spanish
Intermediate
German
Pre-intermediate
French
Pre-intermediate
English
Advanced

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