Heiko Hotz, Developer in London, United Kingdom
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Heiko Hotz

Verified Expert  in Engineering

Artificial Intelligence Developer

London, United Kingdom
Toptal Member Since
November 9, 2022

An AI and ML expert with over 20 years of experience in the IT industry, Heiko's experience encompasses software engineering, IT consulting, and data science. He works with technology leaders to help increase organizations' AI/ML adoption, creates cutting-edge AI demos and solutions, and writes blog posts about the state of the art in the space. Heiko is a recognized thought leader in the NLP domain with extensive public speaking experience.


AI/ML Consulting
Artificial Intelligence (AI), Machine Learning...
Amazon Web Services (AWS)
Machine Learning, Artificial Intelligence (AI)...
Artificial Intelligence (AI), Machine Learning, Data Science, GPT...




Preferred Environment

Amazon Web Services (AWS), Amazon SageMaker, Python 3, PyCharm, Artificial Intelligence (AI), Amazon Machine Learning, Data Science, Hugging Face, Natural Language Processing (NLP), GPT, Generative Pre-trained Transformers (GPT), Natural Language Understanding (NLU), Python

The most amazing...

...application I've developed is a natural language generation demo for the AWS Summit that enabled customers to converse with an AI model.

Work Experience

Freelance Consultant

2022 - PRESENT
AI/ML Consulting
  • Developed a strategy bot for an NGO that generates strategy papers for social entrepreneurs.
  • Created the competency matrix for the AI and Data Science Guild for one of the Big Four.
  • Co-organized a hackathon for an NGO where 20 data scientists helped acquire insights from the organization's data.
Technologies: Artificial Intelligence (AI), Machine Learning, GPT, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Python, Deep Learning, Data Science, Amazon Web Services (AWS), Data Inference, Fine-tuning, Large Language Models (LLMs)

AWS Senior Solutions Architect for AI and Machine Learning

2020 - PRESENT
Amazon Web Services (AWS)
  • Onboarded one of the largest global pharmaceutical companies onto the AWS ML platform, SageMaker.
  • Ensured customer success by developing a custom-made solution based on the Hugging Face ecosystem, growing annual recurring revenue from $300,000 to $1.9 million.
  • Enabled natural language generation with Hugging Face on AWS for one of the largest global pharmaceutical companies.
  • Launched NLP in AWS and grew the community to over 350 active members to scale and coordinate NLP initiatives within AWS, publishing regular newsletters and resources and making them available externally.
Technologies: Machine Learning, Artificial Intelligence (AI), GPT, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Python, Deep Learning, Data Science, Amazon SageMaker, Amazon Web Services (AWS), Language Models, Text Generation, PyTorch, Data Inference, DeepSpeed, Fine-tuning, Causal Inference, Large Language Models (LLMs)

Senior Data Scientist

2017 - 2020
  • Designed and implemented ML models to identify contact and concession reduction opportunities in EU customer service.
  • Identified net concession reduction opportunities of €68 million annually EU-wide.
  • Led all projects and initiatives regarding business intelligence, data analytics, and automation for the team.
  • Created and implemented a machine learning model combining several algorithms to predict future charges for the UK warehouses.
  • Ran a series of back-tests to validate the model's accuracy performance.
  • Devised and developed automation programs to pull, consolidate, and combine data from disparate sources.
Technologies: Artificial Intelligence (AI), Machine Learning, Data Science, GPT, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Python, Amazon SageMaker, Amazon Web Services (AWS)

Senior Director in Data Analytics Group

2015 - 2017
FTI Consulting
  • Developed an algorithm using SQL and Python to trace several billion USD in one of the largest embezzlement cases in recent history.
  • Built a model for a global telecommunications provider to predict regulatory fines due to contractual breaches, reconciling data from three disparate databases and converting unstructured, loaded, and analyzed data using SQL and Python.
  • Drove all data-related aspects of complex accounting investigations—including data identification, extraction, and analysis—and extracted, transformed, and loaded several terabytes of structured data.
  • Advised a global bank on data extraction and analytics procedures and developed and implemented an algorithm in MS SQL Server to identify intercompany trades and determine the ultimate external counterparty.
  • Led a multinational development team to implement the algorithm and develop tools for the client to inquire about transactional data.
  • Visualized and presented findings to senior litigators and the Queen's Counsel.
  • Designed a recommendation model using item-based collaborative filtering algorithms and implemented the model on AWS using Apache Spark to run on millions of items.
  • Analyzed data using SQL, Python Pandas, and Tableau and presented findings to legal counsel.
  • Implemented methodology to reconcile front-office trades with risk consolidation data.
Technologies: Python

AI Model | Legal Contract Review Demo

An AI model that reviews legal contracts.

Contract review is the process of thoroughly reading a contract—to understand the rights and obligations of an individual or company signing it—and assessing the associated impact. It is widely viewed as one of the most repetitive and tedious jobs junior law firm associates must perform. It is also expensive and an inefficient use of a legal professional's skills. In this project, I demonstrate how AI can perform this task.

Blog and Code Repo | How to Build Your Own GPT-J Playground

A blog post and code repository that enables readers to deploy their text generation application in their AWS account.

This app democratizes access to state-of-the-art NLP technology and significantly reduces the costs of using it.

Blog | Developing a Strategy Bot for an NGO with GPT-3

I created and implemented an "Impact Strategy Bot" based on OpenAI's GPT-3 model. The goal was to develop an NLP model that generates an interesting strategy for addressing a social problem provided by a prompt from the user.

My task was to create a fine-tuned custom GPT-3 model for an NGO to accomplish this goal. To achieve this, I first analyzed, cleaned, and prepared the more than 4,000 records that the customer provided as a potential training dataset. After diving deeper into the data, I identified potential features that could be used for training GPT-3. I prepared the dataset accordingly and also engineered the appropriate prompts for the model to maximize the likelihood of the model creating interesting strategies. I then trained GPT-3 using OpenAI's API. After a few iterations and testing the custom models with the NGO, I successfully created an NLP model that would generate interesting strategies from a short prompt mentioning a social problem. The model will be implemented by the customer to be used by future social entrepreneurs within their platform.

Blog and Code Repo | How to Use GPT-J for (Almost) Any NLP Task

In this blog post, we will have a look at how we can use the open-source GPT-J model for a variety of NLP tasks, including text summarisation, email creation, sentiment analysis, and code generation. We use different parameters that are common for text generation applications and particular prompts (i.e., prompt engineering) for the GPT-J model.

Blog and Code Repo | Building a Modular Reasoning, Knowledge, and Language (MRKL) System

Although large language models (LLMs) like ChatGPT are remarkable, they come with significant constraints, such as knowledge cut-offs and hallucinations. While this may not be an issue for certain tasks (e.g., using them to generate imaginative content like a novel), there are times when customers and organizations require precise information from LLMs. Rather than conducting direct searches using tools like web search engines, they prefer to access this data via a unified interface (e.g., in a conversational style). In such cases, the accuracy of the underlying LLM is critical. This tutorial demonstrates how to overcome these limitations, allowing you to harness the full potential of LLMs.




Amazon SageMaker, PyCharm


Data Science


Amazon Web Services (AWS)


Machine Learning, Artificial Intelligence (AI), Amazon Machine Learning, Machine Learning Operations (MLOps), Natural Language Processing (NLP), Natural Language Understanding (NLU), Transformers, Hugging Face, OpenAI, Deep Learning, Stable Diffusion, GPT, Generative Pre-trained Transformers (GPT), Neural Networks, Deep Neural Networks, Large Language Models (LLMs), Language Models, Text Generation, Data Inference, Fine-tuning, Causal Inference, ChatGPT, Programming, Business, DeepSpeed, Proof of Concept (POC)



2013 - 2015

Master's Degree in Business Administration

London Business School - London, United Kingdom

2000 - 2008

Master's Degree in Physics and Computer Science

Ludwig Maximilian University of Munich - Munich, Germany


AWS Certified Solutions Architect – Associate

Amazon Web Services

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