weet.ai

Wearing multiple hats (ML Engineer, AI Engineer, Solution Architect) to provide technical implementation, architecture, guidance and advisory services around the following topics and technologies:

- Large Language Models (LLMs)
- Vector Databases
- Retrieval Augmented Generation (RAG)
- Prompt Engineering
- Prompt Injection Mitigation & Guardrails
- LLM Observability
- RAG Evaluation
- LLM Evaluation
- LLMOps
- Cloud & AI Architecture
- AI Security
- Open Source LLMs / Hugging Face
- OpenAI & ChatGPT
- Azure Databricks & MLflow
- Infrastructure as Code (IaC) & Terraform
- European AI Act
- Gen AI & LLM Red Teaming

Principal Gen AI Engineer

Fractional CTO & Technical Advisor For Startups

I have helped many startups & companies understand, fine tune and deploy their Large Language Models and Gen AI applications following Software Engineering, MLOps and LLMOps best practices.

My biggest learning during this journey was that enterprises face both technical and business challenges when trying to leverage Generative AI and Large Language Models.

This was my biggest motivation for starting weet.ai: helping startups & companies navigate this complex landscape and unlock unique use cases powered by Generative AI.

Keywords: Python, PyTorch, Generative AI, LLMs, MLOps, Machine Learning Engineering, Artificial Intelligence, LLMOps, Kubernetes, MLflow, Weights & Biases, Hugging Face, Docker, FastAPI, Terraform, Azure, AWS, GCP, Apache Spark, Apache Kafka, ReactJS, Langchain, LlamaIndex, Text Generation Inference (TGI), vLLM

Founder

O'Reilly

Technical reviewer for the upcoming book "Hands-on Large Language Models" by Jay Alammar (Cohere) and Maarten Grootendorst (creator of BERTopic).

Topics covered:

- Build advanced LLM pipelines to cluster text documents and explore the topics they belong to
- Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers
- Learn various use cases where these models can provide value
- Understand the architecture of underlying Transformer models like BERT and GPT
- Get a deeper understanding of how LLMs are trained
- Optimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning

To be released in December 2024

Technical Reviewer

Hugging Face

As part of Hugging Face I'm currently developing and growing the following products and initiatives:

- Inference Endpoints
- Text Generation Inference (TGI)
- Autotrain
- Hugging Face on AWS & AWS SageMaker
- GCP & Vertex AI
- Hugging Face on Azure
- NVIDIA DGX Cloud / NVCF Serverless Training

Keywords: Docker, Kubernetes, AWS, AWS SageMaker, Transformers, PyTorch, Unit Testing, CICD, Large Language Models, Generative AI, Machine Learning Engineering, FastAPI, ReactJS, Svelte

ML & Software Engineer

Databricks

- Global SME in the fields of MLOps, Gen AI and LLMs

- Data and AI Summit Trainer for Machine Learning Engineering on Databricks

- Speaker at Big Data London 2022: Fine Tuning and Deploying Chatbots on Databricks with Hugging Face and MLflow

- As a Specialist Solutions Architect, I have a heavy hands-on focus on helping customers achieve the best outcomes from AI and Machine Learning. Specialties:

- MLOps
- Machine Learning Engineering
- Deep Learning
- Computer Vision
- Natural Language Processing
- Natural Language Understanding
- Large Language Models (LLMs)
- Generative AI

In terms of tech stack, my work usually entails:

- PyTorch
- PyTorch Lightning
- Hugging Face
- MLflow
- Apache Spark & SparkML
- Apache Kafka
- Scikit-Learn
- Terraform
- Petastorm
- Horovod
- Deepspeed
- FastAPI
- Kubernetes

Specialist Solutions Architect Gen AI & ML Engineering

At ING, I was part of the Global Customer Dialogue Analytics CoE. 

My role was to leverage my Data Science, Data Engineering, Machine Learning Engineering, Large Language Models (LLMs) and Solution Architecture skills in order to deliver the customer service of the future.

Keywords: Data Engineering, Python, Docker, Kubernetes, OpenShift, Helm, Apache Airflow, Machine Learning Engineering, MLOps, Natural Language Processing (NLP), CICD, Chatbots, Automatic Speech Recognition (ASR), Tensorflow, PyTorch, Hugging Face, Kaldi, Named Entity Recognition (NER), Sci-Kit Learn, spaCy, Postgres, LLMs

Senior Machine Learning Engineer | Customer Service AI

ABN AMRO Bank N.V.

As part of Chief Architecture and Data Management team, my role was to architect and implement a bank wide Data Orchestration Platform based in Apache Airflow. 

This was structured as a PaaS offering on top of Kubernetes, so that multiple domain data stores within ABN could onboard their orchestration workloads in a seamless manner. This was a critical step in the road towards the implementation of a Data Mesh architecture.

Consulting assignment through Altius/Avanade.

Keywords: Azure, Python, Data Engineering, Databricks, Docker, Kubernetes, Helm, Pyspark, Apache Airflow, CICD, Hashicorp Vault, Data Mesh

Data Engineer

Ahold Delhaize

At Albert Heijn my role was to create an Enterprise Data Lake for customer data, which allowed data scientists, data analysts, marketers and other business users to unlock unique business insights and opportunities.

Consulting assignment through Altius/Avanade.

Keywords: Azure, Delta Lake, Data Lake, Data Engineering, Databricks, Python, Apache Spark, Pyspark, Hashicorp Terraform, Infrastructure as Code, CICD

Vattenfall

Worked as a consultant within the Renewable Energy Forecasting and Optimization department in Hamburg, Germany, where my main objective was to create a streamlined, realtime data platform based in microservices, with the focus of taking the Digital Power Purchase Agreement (PPA) - day-ahead and intraday energy trading business to the next level

Consulting assignment through Sia Partners.

Keywords: Azure, Python, Docker, Kubernetes, Machine Learning, Sci-Kit Learn, Gradient Boosted Decision Trees, LightGBM, CICD, DevOps

Bradesco

Managed IT products in order to implement digital change and transformation within Private Banking and Foreign Exchange departments in Argentina, Luxembourg, Cayman Islands and New York.

Product Manager FX & Private Banking IT

HSBC Global Banking and Markets

Managed the IT products for Equities Derivatives & Delta One and Broker Dealer (Cash Equities, Futures and Derivatives) departments, including initiatives such as algorithmic trading strategies (futures, index and ETF arbitrage) & platforms, anti-money laundering, front office / trading, risk and back office / post trading platforms.

Keywords: High Frequency Trading (HFT), Direct Market Access (DMA), Python, FIX Protocol, Apama, Complex Event Processing

Product Manager High Frequency Trading & Broker Dealer

Gen AI | LLMs | Machine Learning Engineering | AI Engineering | Azure | AWS | CKAD | MLOps | LLMOps

I have a strong background in software engineering, cloud architecture, solution architecture, machine learning engineering, generative AI, large language models, MLOps and LLMOps.

Some of the companies that I've worked with in the past include Hugging Face, Databricks, ING Bank, ABN Amro, Koninklijke Ahold Delhaize, Vattenfall AB and HSBC Bank - in Freelance, FTE and Contract capacities.

My tech stack currently includes:

- Python
- Rust
- DSPy
- Langchain
- LlamaIndex
- Autogen 
- Crew AI
- Phoenix Arize
- MLflow
- Python
- React JS & TypeScript
- Terraform
- Azure Databricks
- Docker
- Kubernetes
- Vector DBs
- Pytorch
- Apache Spark 

Last but not least, I am a huge believer of teamwork, learning, teaching, autonomy, accountability, user-centricity, and data-driven decisions. I am passionate about learning new technologies and advancing the state of the art in data and AI.

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🚀 Rafael V. Pierre, MSc.

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