GenAI Expert
- Verfügbarkeit einsehen
- 0 Referenzen
- 75€/Stunde
- 10178 Berlin
- Weltweit
- en | de
- 14.10.2024
Kurzvorstellung
Qualifikationen
Projekt‐ & Berufserfahrung
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.
Generative KI, GPT, Langchain, Language Integrated Query, Large Language Models
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
Datenmodelierung, Generative KI, Large Language Models, Pytorch, Natural Language Processing, SISR (Semantic Interpretation for Speech Recognition), SRGS (Speech Recognition Grammar Specification)
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.
Generative KI, GPT, Langchain, Large Language Models, Language Integrated Query
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
Natural Language Processing, Python, Pytorch
2/2023 – 3/2023
TätigkeitsbeschreibungSummary 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 QualifikationenNatural Language Processing, Python, Pytorch, Spracherkennung, Text Mining, Textklassifikation
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
Cloud (allg.), Python
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.
Apache Spark, Business Intelligence (BI), Datenanalyse, ETL, Keras, Natural Language Processing, Postgresql, Python, Pytorch, Tensorflow, Textklassifikation
3/2021 – 4/2021
TätigkeitsbeschreibungImplemented 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 QualifikationenKeras, Natural Language Processing, Python, Tensorflow
2/2021 – 2/2021
TätigkeitsbeschreibungImplemented 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 QualifikationenDjango, Natural Language Processing, Selenium
1/2021 – 1/2021
TätigkeitsbeschreibungImplemented a search engine for 3D printable models. The platform is live at 3dfindabledotcom. Tools used are Python | Django| Selenium | VGG16.
Eingesetzte QualifikationenDjango, Natural Language Processing, Selenium
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
Apache Hadoop, Gradient Boosting, Keras, Natural Language Processing, Python, Rekurrentes Neuronales Netzwerk (RNN), Scikit-learn, Support Vector Machine
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
Apache Hadoop, Apache Spark, Big Data, Elasticsearch, Google Analytics, Maschinelles Lernen, Power Bi, Python, R (Programmiersprache), Scikit-learn, Tableau
2/2016 – 2/2016
TätigkeitsbeschreibungWorked as an application to develop battery softwares
Eingesetzte QualifikationenC, C++, Python
Zertifikate
Ausbildung
Alpen-Adria-Universität Klagenfurt, Österreich
RWTH Aachen, Germany
UET Lahore, Pakistan
Über mich
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
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
- Englisch (Muttersprache)
- Deutsch (Gut)
- Europäische Union
- Schweiz
- Vereinigte Staaten von Amerika
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