freiberufler GenAI Expert auf freelance.de

GenAI Expert

zuletzt online vor wenigen Tagen
  • 75€/Stunde
  • 10178 Berlin
  • Weltweit
  • en  |  de
  • 14.10.2024

Kurzvorstellung

With over a decade of experience in AI and machine learning, I have developed deep expertise in GenAI. I have worked on fine-tuning LLMs for various applications.

Qualifikationen

  • Cloud Computing
  • Natural Language Processing1 J.
  • Python6 J.
  • Amazon Web Services (AWS)
  • Data Science
  • Generative KI1 J.
  • Google Cloud
  • Maschinelles Lernen4 J.
  • Predictive Analytics
  • Textklassifikation

Projekt‐ & Berufserfahrung

GenAI Consultant
QPharma Pvt. Ltd., USA, New Jersy
3/2024 – 10/2024 (8 Monate)
Life Sciences
Tätigkeitszeitraum

3/2024 – 10/2024

Tätigkeitsbeschreibung

Main Responsibilities
▪ Fine-tuning large language models (LLMs) using the DPO-ORPO approach to enhance document tagging
accuracy in the medical domain.
▪ Implemented a RAG based chat model, enabling customers to interact with the backend database through
natural language queries.

Eingesetzte Qualifikationen

Generative KI, GPT, Langchain, Language Integrated Query, Large Language Models

R&D Consultant on S2ST, TTS, ASR
Qonda GmbH, DE, Berlin
2/2024 – 3/2024 (2 Monate)
IT & Entwicklung
Tätigkeitszeitraum

2/2024 – 3/2024

Tätigkeitsbeschreibung

Wrote the two research proposals to be presented to Investment Bank Berlin (IBB):
I. Flow normalization using multi-stage single-channel speech enhancement approach in ASR.
II. Optimizing S2ST: Model Compression and Data Augmentation for Efficient Speech-to-Unit Translation

Eingesetzte Qualifikationen

Datenmodelierung, Generative KI, Large Language Models, Pytorch, Natural Language Processing, SISR (Semantic Interpretation for Speech Recognition), SRGS (Speech Recognition Grammar Specification)

GenAI Consultant
NDI GmbH, CH, Zug
10/2023 – 2/2024 (5 Monate)
IT & Entwicklung
Tätigkeitszeitraum

10/2023 – 2/2024

Tätigkeitsbeschreibung

Main Responsibilities
▪ Implemented a RAG approach together with an LLM to perform content management.
▪ Implemented a RAG based chat model to help resolve customer queries of an online gambling platform.

Eingesetzte Qualifikationen

Generative KI, GPT, Langchain, Large Language Models, Language Integrated Query

SPECIALIZED CHAT BOT FOR A INSURRANCE COMPANY
Kundenname anonymisiert, Wien
4/2023 – 7/2023 (4 Monate)
Versicherungen
Tätigkeitszeitraum

4/2023 – 7/2023

Tätigkeitsbeschreibung

Fine tuning of an LLM (meta/Llama_2) to improve customer queries and Q&A. Respond more consistently, learning focus, reduce hallucination, gain knowledge on insurance data. Vector averaging of word embeddings was used to measure model performance.

Llama_2 | Hugging Face | Pytorch | Embedding Distance | LAMINI | FastAPI | Pandas | Numpy

Eingesetzte Qualifikationen

Natural Language Processing, Python, Pytorch

Market researcher on LLM
Ministry of higher education Pakistan, Wien
2/2023 – 3/2023 (2 Monate)
IT & Entwicklung
Tätigkeitszeitraum

2/2023 – 3/2023

Tätigkeitsbeschreibung

Summary review on LLM concepts, including private and open-source models, comparison of model building architectures, various MT benchmarks (HellaSwag, HumanEval), various LLM studios (H20, LAMINI) offering such services, and ethics and trustworthiness aspects of it.

Eingesetzte Qualifikationen

Natural Language Processing, Python, Pytorch, Spracherkennung, Text Mining, Textklassifikation

Research Data Scientist
Max Planck Institute of Plasma Physics, Greifswald
6/2022 – 12/2022 (7 Monate)
Hochschulen und Forschungseinrichtungen
Tätigkeitszeitraum

6/2022 – 12/2022

Tätigkeitsbeschreibung

Automated Scientific Discovery – Use of ML methods to ease the cycle of scientific discovery. Implemented an automated ML platform with several explainable AI and causality frameworks.
Tools:
Dask | Ray | Pytorch | Python | Optuna | LIME| SHAP | Anchor | ASV | SHAPLEY FLOW | Captum | FastAPI

Eingesetzte Qualifikationen

Cloud (allg.), Python

Full Stack Data Scientist
IU International University of Applied Sciences, München
3/2022 – 6/2022 (4 Monate)
Hochschulen und Forschungseinrichtungen
Tätigkeitszeitraum

3/2022 – 6/2022

Tätigkeitsbeschreibung

Development of a feature-store to help boost model development process. Automation of machine learning models using the complete MLOps stack of AWS including CI/CD, model pipelining, deployment, and scheduling.
ETL scripts to load data google analytics, flatten the key/value structure of the data, and finally save it in AWS S3. Built a model for text labeling to better understand student feedbacks.

Eingesetzte Qualifikationen

Apache Spark, Business Intelligence (BI), Datenanalyse, ETL, Keras, Natural Language Processing, Postgresql, Python, Pytorch, Tensorflow, Textklassifikation

Lead Developer
FetchCFD UG, Erlangen
3/2021 – 4/2021 (2 Monate)
Web
Tätigkeitszeitraum

3/2021 – 4/2021

Tätigkeitsbeschreibung

Implemented a Twitter bot which search through tweets on topics of interest, summarizes the text on those tweets, perform sentiment analysis and then publish the story on the timeline. Found @empirisch2. Tools used were Python| Tweepy |NLTK | Hugging Face T5 | Sentiment Analysis

Eingesetzte Qualifikationen

Keras, Natural Language Processing, Python, Tensorflow

Lead Developer
FetchCFD UG, Erlangen
2/2021 – 2/2021 (1 Monat)
Search Engine
Tätigkeitszeitraum

2/2021 – 2/2021

Tätigkeitsbeschreibung

Implemented a search engine for DIY projects. The platform is live at findingdiydotcom. Tools used were Python| Django | Selenium | Hugging Face T5 | NLTK | Sentiment Analysis.

Eingesetzte Qualifikationen

Django, Natural Language Processing, Selenium

Lead Developer
FetchCFD UG, Erlangen
1/2021 – 1/2021 (1 Monat)
Machine Learning
Tätigkeitszeitraum

1/2021 – 1/2021

Tätigkeitsbeschreibung

Implemented a search engine for 3D printable models. The platform is live at 3dfindabledotcom. Tools used are Python | Django| Selenium | VGG16.

Eingesetzte Qualifikationen

Django, Natural Language Processing, Selenium

Full Stack Data Scientist
Kundenname anonymisiert, Wien
3/2019 – 8/2019 (6 Monate)
Data Science
Tätigkeitszeitraum

3/2019 – 8/2019

Tätigkeitsbeschreibung

STOCK PRICE PROJECTIONS
Price forecasting of blue-chip stocks.
Python | SQL | LSTM | RNN | Boosting | SVM | GNU | Scikit-Learn | Keras | FBProphet | Optuna | State-Space Modeling | ARIMA | Kalman Filter | ta-lib

Eingesetzte Qualifikationen

Apache Hadoop, Gradient Boosting, Keras, Natural Language Processing, Python, Rekurrentes Neuronales Netzwerk (RNN), Scikit-learn, Support Vector Machine

Lead Full Stack Data Scientist (Festanstellung)
T-Mobile GmbH, Vienna, Austria, Wien
4/2017 – 12/2021 (4 Jahre, 9 Monate)
Data Science
Tätigkeitszeitraum

4/2017 – 12/2021

Tätigkeitsbeschreibung

MODELS FOR CROSS-SELLING (02.2021 - 11.2021)
Several machine learning models are built to help facilitate the cross-selling activities of a telecom operator. The models are built to, 1) identify the likely customer for cross-selling, 2) the most suitable product for that customer and lastly, 3) the right channel to approach that customer. Campaign success rate increased by 17%.
Python | SQL | DWH | CI/CD | Oozi | Tableau | Google Analytics | Big Query | Boosting | Bagging | Optuna


MODELS FOR UP-SELLING (03.2021 - 08.2021)
Built machine learning models to help facilitate the up-selling activities. This is to facilitate the base-management challenges, i.e., increase ARPU per customer by up-scaling their tariffs. Campaign success rate increased by 28%.
Python | SQL | DWH | CI/CD | Oozi | Tableau | Google Analytics | Big Query | Scikit-Learn | Optuna

LIFT AND SHIFT 08.2020 - 01.2021
The end-to-end execution of household identification use-case was too time consuming and was block other pipelines. Therefore, a lift and shift approach was adopted to fully migrate the use-case to the GCP, including its ETL.
Pyspark | Cloud Storage | GCP Dataproc | GCP Data Studio | GCP Cloud Composer | AirFlow

HOUSEHOLD IDENTIFICATION (04.2020 - 01.2021)
Built a model to identify customers living in same household for a telecom operator. Extensive explorative data analysis is performed to identify potential levers to help improve the matching criteria.
Pyspark | Hive | SQL | CI/CD | Oozi | Tableau | Google Analytics | Big Query | MLIB | Graph Theory


PROACTIVE MAINTENANCE (07.2019 - 03.2020)
Proactively detect the root causes of network problems. A thorough time-series analysis is performed and both induction (ML) as well as deduction-based models are built. Time to resolve the ticket was reduced by half.
Pyspark | Hive| SQL | Power BI | CI/CD | Oozi | Tableau | ElasticSearch


TARIFF OPTIMIZATION (02.2019 - 05.2019)
Sketch a relation between the old products and current customer base and identify any gaps in the current portfolio which hinders the operators in increasing their customer base. Additionally, analyze the need for any shadow products for retention and customer base management.
R | R FOR OPERATIONS RESEARCH | Google ortools | Crone | R&D| Clustering


VALUE BASED NETWORK ROLL-OUT (05.2018 - 02.2019)
Based on several customer satisfaction KPIs, a model is built to efficiently target network rollout and upgrade activities by investing in sites with poor network quality scores. This results in saving of several million euros as network rollout is a high budget activity i.e., a small % improvement in investment strategy results in considerable savings.
Pyspark | Hive | Power BI | Traffic Forecasting | xgboost | Oozi | R&D | Analytical Models

CUSTOMER EXPERIENCE (09.2017 - 05.2018)
Several models are built to measure customer experience on various service aspects. E.g., customer satisfaction via NPS, bill shock, customer experience w.r.t network quality, as well as service line interactions. Several inductions (ML) and deduction-based models are built.
Pyspark | Hive | Python | SQL | Power BI |Oozi | Analytical Models


CHURN & RETENTION (05.2017 - 09.2017)
Built a model to predict customer churn. Additionally, a detailed analysis on churn reasons is performed by calculating the SHAPLEY values. After A/B testing, an ultimate 17% reduction in churn was achieved.
Python | SQL| Power BI | DNN | Boosting | SVM | Bayesian Statistics | SHAPLEY | Crone Job | Scikit-Learn | Keras


TRAFFIC LOAD FORECASTING (04.2018 - 07.2020)
Forecasting network traffic per cell basis. This project was the pre-requisite of value-based roll-out.
Python | SQL| LSTM| RNN | Boosting | SVM | GNU | Scikit-Learn | Keras | FBProphet | Optuna | State-Space Modeling | ARIMA | Kalman Filter | DeepAR | AWS SageMaker

Eingesetzte Qualifikationen

Apache Hadoop, Apache Spark, Big Data, Elasticsearch, Google Analytics, Maschinelles Lernen, Power Bi, Python, R (Programmiersprache), Scikit-learn, Tableau

Application Developer (Festanstellung)
Samsung, Graz
2/2016 – 2/2016 (1 Monat)
IT & Entwicklung
Tätigkeitszeitraum

2/2016 – 2/2016

Tätigkeitsbeschreibung

Worked as an application to develop battery softwares

Eingesetzte Qualifikationen

C, C++, Python

Zertifikate

Modernizing Data Lakes and Data Warehouses with GCP (Coursera)
2022
Building Batch Data Pipelines on GCP (Coursera)
2022
Building Resilient Streaming Analytics Systems on GCP (Coursera)
2022
Google Cloud Big Data and Machine Learning Fundamentals (Coursera)
2021
Building a Data Science Team (Coursera)
2018
Structuring Machine Learning Projects (Coursera)
2018
Neural Networks and Deep Learning (Coursera)
2018
Improving Deep Neural Networks (Coursera)
2018
Convolutional Neural Networks (Coursera)
2018
Fundamentals of Machine Learning in Finance (Coursera)
2018
Guided Tour of Machine Learning in Finance (Coursera)
2018
Finance for Non-Finance Professionals (Coursera)
2018

Ausbildung

IT
PhD
2017
Alpen-Adria-Universität Klagenfurt, Österreich
IT
MSc
2011
RWTH Aachen, Germany
IT
BSc
2007
UET Lahore, Pakistan

Über mich

With over a decade of experience in AI and machine learning, I have developed deep expertise in Generative AI (GenAI). I have worked on fine-tuning large language models (LLMs) for various applications, including document tagging and customer query resolution, using cutting-edge technologies like PyTorch, Hugging Face, and LangChain. My experience spans building GenAI systems for content management and interactive chat models, optimizing models for real-world applications, and staying at the forefront of emerging technologies in the GenAI landscape.

I have ample experience of interacting with C-level executives on progress updates and strategy road maps. I have also led a group of senior researchers from universities on several projects about model bias, privacy, and explainable AI. I have also been the part of the special committee designing the nation AI policy of Pakistan, reporting directly to the prime minister of the country.

Weitere Kenntnisse

Azure Container Apps/Instances, Machine Learning, Data Factory, Function Apps, Blob | AWS Lambda, S3, RedShift, SageMaker | Python | PySpark | Dask | Ray | SQL | Airflow | Google Analytics, BigQuery, Data Studio, Bucket | Git |
CI/CD Power BI | Django, Flask, FastAPI | Angular Typescript | PostgreSQL | Pytorch | Hugging Face | LangChain | NLTK Gensim | SpaCy | OpenAI | Jira Kanban Confluence | Bagging | Boosting| Clustering | Deep Neural Networks | Explainable AI (SHAPLEY, SVM, etc.) | Bayesian Optimization | Time Series Models (GARCH, ARIMA, etc.), GlounTS | Transformers (Hugging Face) | RAG | LlamaIndex | Agents | QLoRA | Vector Database Pine

Persönliche Daten

Sprache
  • Englisch (Muttersprache)
  • Deutsch (Gut)
Reisebereitschaft
Weltweit
Arbeitserlaubnis
  • Europäische Union
  • Schweiz
  • Vereinigte Staaten von Amerika
Home-Office
bevorzugt
Profilaufrufe
1268
Alter
40
Berufserfahrung
12 Jahre und 7 Monate (seit 04/2012)
Projektleitung
2 Jahre

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