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4-6 years
6-10 years
10-15 years
More than 15 years
Data Engineer @Groundhog Technologies Inc.2021 ~ 2024
・
Data Analyst、Data Engineer、Data Scientist、Customer Experience Analyst
Within one month
Employed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
University of Illinois at Urbana-Champaign, School of Information Sciences
・
Information Management
Past
Service Provider @Taron Solutions Limited2023 ~ 2023
・
AI工程師、機器學習工程師、電腦視覺工程師、資料科學家、Machine Learning Engineer、Computer Vision Engineer、Data Scientist
Within one month
型 side project 使用深度學習框架(Pytorch)自行搭建預測模型，以各式台灣景氣指標當作輸入，輸出未來經濟景氣趨勢階段。 技術：Pytorch、Pandas、Numpy、 Sklearn 論文發表 • Chen, X. Z., Li, M. C. , & Chen, Y. L, January). Strategies for Helping Anchor-Based Trackers Learn re-ID Features for Smart City Surveillance. In 2024 IEEE International Conference on Consumer Electronics (ICCE) (ppIEEE. • Li, M.
Unemployed
・
Ready to interview
Full-time / Interested in working remotely
4-6 years
國立臺北科技大學
・
資訊工程
Data Analyst & IoT Software Developer @FARAZ ERTEBAT2021 ~ Present
・
Computer Vision / Deep Learning
Within one month
Sayed Mohammad Ahmadi Python Developer | Deep Learning | Computer Vision Specialist | C++ & C# Developer | Lifelong Learner | 7+ Years of Experience Qom, Qom Province, Iran I am a Python Developer with expertise in Deep Learning and Computer Vision, backed by 7+ years of experience. Skilled in C++ and C# as well, I develop robust solutions that encompass various programming paradigms. Passionate about staying up-to-date with the latest advancements, I excel at leveraging AI and computer vision techniques to derive valuable insights from visual data. I have successfully applied my
Employed
・
Open to opportunities
Full-time / Interested in working remotely
4-6 years
University of Qom
・
Information Technology | Face Recognition
Past
機器學習工程師 @順豐科技公司 2021 ~ 2022
・
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Within one month
和集群運行原理 ，與 Hive、 Presto組件 ，熟練掌握SQL語言編寫與模型落地，具有其調優經驗 ； 熟練使用Python與Pyspark， 熟練 使用RDD算子與Spark SQL、Sklearn等，進行機器學習與數據分析 ； 熟練使用Git與Docker，能進行YAML、Dockerfile編寫與CICD部署流程、搭建RESTful API等 ； 熟練使用 Tableau 與 Power BI ，熟練使用
Unemployed
Full-time / Interested in working remotely
4-6 years
國立政治大學（National Chengchi University）
・
統計系
AI 工程師 @台灣塑膠工業股份有限公司2020 ~ Present
・
AI工程師、機器學習工程師、深度學習工程師、資料科學家、Machine Learning Engineer、Deep Learning Engineer、Data Scientist
Within six months
Selenium Cloud Microsoft Azure Azure TSI Azure Power BI Azure ML Azure App Service Azure Form Recognizer Azure Cognitive Services Google Cloud Platform Google Cloud GPU Google Cloud VM Google Cloud AutoML Google Cloud Vision API Other Cloud Source Heroku App Service Line Bot ML/AI AI Tool TesnsorFlow SKlearn Keras Matlab OpenCV Algorithm Principal Component Analysis Partial Least Squares Gaussian Process Deep Neural Networks Convolutional Neural Networks Genetic Algorithm Other Application Reinforcement Learning Object Detection Faster RCNN Yolo v3 Generative Adversarial Networks Experience 台灣塑膠工業(FPC) - AI Engineer - Sept~ present 1. AI應
Employed
Full-time / Interested in working remotely
4-6 years
Chung Yuan Christian University
・
Master in Chemical Engineering
Active
Active
人工智慧工程師 @玉山銀行智能金融處 (Intelligent Finance Dept., E-SUN Bank )2020 ~ Present
・
Machine Learning Researcher / Engineer
Within one month
Jeffrey Lin ( 林奕勳 ) ESUN Bank, Intelligent Finance Technology Department, AI Engineer focusing on Enterprise-Level ML Frameworks and Design Patterns [email protected] TW, Taipei CAREER SUMMARY 6+ years of deep learning research and development experience, with a strong ability in prototyping, evaluation, and deployment of models, in both academical and industrial environment. 2+ years of financial AI development experience, specialized in the design of python API, SDK, and framework for optimizing the deployment flow of data science, ETL, and machine learning pipelines. 1+ year of participation in the technology management
Employed
Full-time / Interested in working remotely
4-6 years
National Taiwan University
・
Graduate Institute of Communication Engineering
Data scientist
Within three months
理解不同的領域知識便於進行分析 金融商品分析、個股交易、選擇權、期貨交易、加密貨幣交易經驗 Skill Web Crawling Python Data Mining Python - numpy, pandas, missingno, sk-learn Weka Machine Learning Python - Logistic regression, Decision Trees, Random forest, GridSearchCV Game Design Game Development Game Design User Experience Design Market Research Project Management Others MySQL Linux, Hadoop, Spark, Pyspark HTML, Java script Jira, Teambition Work Experience Daohe Ltd. Producer ，2021//12 Genetopia（休閒
Full-time / Interested in working remotely
10-15 years
MCU
・
統計系Data Scientist/ Engineer @Intomarkets2022 ~ Present
・
Data Scientist
More than one year
Full-time / Interested in working remotely
6-10 years
University of Paderborn
・
Master of Science in Computer
Senior Machine Learning Engineer @C-Media Electronics2020 ~ 2021
・
Machine Learning Scientist, Data Scientist
Within one month
Hao-Chun (Chad) Yang Ph.D Ph.D Graduate @NTHU (EE) | Seeking AI/ML R&D Position | Speech, IOT, Health Informatics, Computational Neuroscience | pytorch, tensorflow Room 315, General Building III, No. 101, Section 2, Kuang-Fu Road,Hsinchu City, Taiwan Skills Programming Programming: Python, Matlab DevOps: AWS, GCP, Git, Docker Deep Learning: Pytorch, Tensorflow, Keras ML& Data Science: Sklearn, Numpy, Pandas, Matplotlib MLOps: MLflow, W&B Special HonorsBest Challenge Poster - Physionet/CINC ChallengeTravel Grants - IEEE SPS SocietyPresident Scholarship - NTHU Education National Tsing Hua University Ph.D. in Electrical Engineering (SepPresent) National Tsing
In military service
Full-time / Interested in working remotely
4-6 years
清華大學
・
電機工程Employed
・
Not open to opportunities
Full-time / Interested in working remotely
6-10 years
成功大學
・
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Within one month
AI Engineer
玉山銀行智能金融處 (Intelligent Finance Dept., E-SUN Bank )
・
2020 ~ Present
Professional Background
Current status
Employed
Job Search Progress
Professions
Data Engineer, Machine Learning Engineer
Fields of Employment
Software, Banking
Work experience
1-2 years work experience (4-6 years relevant)
Management
I've had experience in managing 1-5 people
Skills
Languages
English
・
Intermediate
Job search preferences
Positions
Machine Learning Researcher / Engineer
Job types
Full-time
Locations
台灣台北市, 台灣新竹市
Remote
Interested in working remotely
Freelance
Yes, I freelance in my spare time
Educations
School
National Taiwan University
Major
Graduate Institute of Communication Engineering
CAREER SUMMARY
- 6+ years of deep learning research and development experience, with a strong ability in prototyping, evaluation, and deployment of models, in both academical and industrial environment.
- 2+ years of financial AI development experience, specialized in the design of python API, SDK, and framework for optimizing the deployment flow of data science, ETL, and machine learning pipelines.
- 1+ year of participation in the technology management assistant bootcamp, with real-experience on business user support, gaining first-hand insight on financial and corporative AI usage via cross-department collaboration.
- Having excellent code-quality standard, efficiency-oriented thinking, and perseverance in face of challenging problems; Continue absorbing “technical-nutrients” from the development of open-source AI community.
PROJECT
Next-Generation ETL Framework for Data Science Team
2022/7-2022/12
- Goal:
- Design a lightweight ETL python framework enabling data scientists to seamlessly deploy their pandas/SQL processes to airflow in a maintainable format.
- Reduce the maintenance and optimization cost of AI projects with integration of customizable data validation, metadata registration, and time/memory-profiling tools.
- Ensure flexibility and extensibility in pandas-like packages (such as CuDF, Polars, PySpark, etc) for future parallelizability, targeting 1000X speedup.
- Responsibility: Coordinate the design and development of ETL framework components. Give instruction to ETL research intern and provide code review and design guidance for the project participants.
- Accomplishment: Have wrapped the framework into a workable python package and successfully imported it into two production projects, with ETL business logic automatically rendered on the Airflow UI in an easily traceable way.
Enhance and Maintain House Price and Mortgage Loan API
2022/2-2022/7
- Responsibility: API Service maintenance, code refactoring, and model performance enhancement for the house-price/mortgage loan automatic appraisal system.
- Accomplishment:
- Encrypted API re-routing:
- Re-route the internal mortgage API service as an encrypted API service for external client. Understand and implement the RSA/AES double encryption protocol under spec-ambiguous situation by investigating client-side PHP code.
- Code refactor and migration:
- Reduce the cognitive complexity of service code from 1000-line-single-file situation by thoroughly understand the business logic and refactoring it into chain-of-command design pattern.
- Design common module with logging mechanism to reduce future maintenance cost.
- Draw and carry out the code migration plan without compromising the 24/7-criteria.
- Re-design of house appraisal model for interpretability:
- Coordinate with research institution and business unit to build a business interpretable deep learning model.
- Re-design the loss function with regularization term to meet the legal requirements.
- Reducing the model inference time by adopting Google’s state-of-the-art nearest neighbor algorithm for referential house searching.
Scalable ETL Pipeline for Credit Loan Marketing Data
2021/8-2022/1
- Responsibility:
- Orchestrate the cross-department coordination of the development process.
- Design a low-code python-SQL interface for the data-analyst of business unit and provide development and CICD guidance.
- Ensure business-logic-extensibility in considerations of future marketing campaign.
- Ensure timely delivery of 7 million customer campaign data under 1day computation criteria.
- Accomplishment:
- Have helped the data-analyst of business unit successfully incorporate new campaign business logic into the production pipeline.
- A comprehensive tutorial to the development interface and CICD toolkits, enabling smooth new feature extension with zero-guidance.
- Significantly speedup the pipeline from 5d to 8h via map-reduce parallelization with multiple Airflow worker nodes.
PyTorch development framework for Industry-Academy Cooperation
2021/4-2022/8
- Goal: Develop Pytorch model development framework to reduce the academy-to-company code migration cost.
- Features:
- Support training, validation, testing, weight/performance inspection, and check-pointing of models with different architecture.
- Fixed experimentation flow for all models with formatted running script and folder structure, to enhance code readability and reproducibility.
- Support lazy evaluation and visualization of data pre-processing, avoid re-generation of data in experimentation scenario.
- Supporting parallel model hyper-parameter tuning via Ray (a general python parallelization framework).
- Tools: Pytorch-lightning, Ray, TensorBoard, pyflow-viz
2D Indoor Positioning System through Android App
2014/1-2015/1
Instructor: Professor Ren-Song Tsay
- Survey on the state-of-the-art indoor positioning technologies and write a project proposal. Investigate the relationship between distance and the amplitude of the Bluetooth signal through experiment. Develop an android app that fuses the signals of Bluetooth and Geo sensor for position estimation.
- Score: A+
RESEARCH
Develop techniques applicable to the music industry and design experiments for proof-of-concept
2015/9-2020/1
Instructor: Professor Homer H. Chen
- Developed a context-based tag propagation method to reduce the training noise and successfully improved a deep music auto-tagging model (SampleCNN) by 25.8% in MAP.
- Enhance the robustness of the tag propagation method and successfully extended the improvement to two other neural networks (CRNN and 2D-CNN) with the increase of improvement reaches 6.5%, 4.9%, and 5.5% (for SampleCNN, CRNN, and 2D-CNN, respectively).
- Responsibility: Build up the experiment acceleration (via GPU) environment on a Linux-based server; Design the model evaluation and training strategy with TensorFlow.
- Conduct error analysis to understand the problem in the training data using Matplotlib and Seaborn. Tune the hyper-parameters during model development. Accelerate the evaluation and data visualization process using ray, a parallel processing package.
Research proposal in music for lab funding from the Ministry of Science and Technology (MOST)
2015/9-2017/10
- Wrote research plans about how lyrics may help to improve the user experience of a music recommendation system.
- Research proposals: 1) Interactive, Context-Aware Music Recommendation and 2) A Unified Framework for Processing and Understanding Heterogeneous Data for Intelligent Recommendation.
EDUCATION
M.S. in Graduate Institute of Communication Engineering, National Taiwan University
2015/9 ~ 2020/1
- Multimedia Signal Processing (A)
- Special Topics on Internet of Things (A): Develop an IOT product and demonstrate its functionality.
- Computational Methods and Tools for Data Science (A): Familiarize myself with basic EDA (exploratory data analysis) techniques including PCA and t-SNE.
B.S. in Electrical Engineering Department, National Tsing Hua University
2011/9 ~ 2015/6
- Programming Classes: Logic Design (A+) / Logic Design Lab (A+) / Embedding System Lab (A+) / Computer Programming Design (A+)
- Math Classes: Calculus I&II (A+/A+) Linear Algebra (A+) / Numerical Analysis (A)
SKILLS
- Programming Skills: Design Patterns、ETL、Image Building、Machine Learning、Parallel Acceleration、Python SDK Design、Refactoring
- Development Environment: Linux, Docker, Oracle-Cloud, Visual Studio Code
- Computer Languages: Python、C++、SQL、JAVA
- Tools for Machine Learning and Data Science: Pytorch、Pytorch-Lightning、CuDF (GPU-accelerating dataframe)、Pandas、Networkx、LightGBM、DeepGraphLibrary
- Tools for Model Serving, Deployment, and Data Pipeline: Airflow、FastAPI、 k8S、PostgresDB、RedisGraph、Neo4j、Ray
For Collaboration: Github、Azure DevOps
CAREER SUMMARY
- 6+ years of deep learning research and development experience, with a strong ability in prototyping, evaluation, and deployment of models, in both academical and industrial environment.
- 2+ years of financial AI development experience, specialized in the design of python API, SDK, and framework for optimizing the deployment flow of data science, ETL, and machine learning pipelines.
- 1+ year of participation in the technology management assistant bootcamp, with real-experience on business user support, gaining first-hand insight on financial and corporative AI usage via cross-department collaboration.
- Having excellent code-quality standard, efficiency-oriented thinking, and perseverance in face of challenging problems; Continue absorbing “technical-nutrients” from the development of open-source AI community.
PROJECT
Next-Generation ETL Framework for Data Science Team
2022/7-2022/12
- Goal:
- Design a lightweight ETL python framework enabling data scientists to seamlessly deploy their pandas/SQL processes to airflow in a maintainable format.
- Reduce the maintenance and optimization cost of AI projects with integration of customizable data validation, metadata registration, and time/memory-profiling tools.
- Ensure flexibility and extensibility in pandas-like packages (such as CuDF, Polars, PySpark, etc) for future parallelizability, targeting 1000X speedup.
- Responsibility: Coordinate the design and development of ETL framework components. Give instruction to ETL research intern and provide code review and design guidance for the project participants.
- Accomplishment: Have wrapped the framework into a workable python package and successfully imported it into two production projects, with ETL business logic automatically rendered on the Airflow UI in an easily traceable way.
Enhance and Maintain House Price and Mortgage Loan API
2022/2-2022/7
- Responsibility: API Service maintenance, code refactoring, and model performance enhancement for the house-price/mortgage loan automatic appraisal system.
- Accomplishment:
- Encrypted API re-routing:
- Re-route the internal mortgage API service as an encrypted API service for external client. Understand and implement the RSA/AES double encryption protocol under spec-ambiguous situation by investigating client-side PHP code.
- Code refactor and migration:
- Reduce the cognitive complexity of service code from 1000-line-single-file situation by thoroughly understand the business logic and refactoring it into chain-of-command design pattern.
- Design common module with logging mechanism to reduce future maintenance cost.
- Draw and carry out the code migration plan without compromising the 24/7-criteria.
- Re-design of house appraisal model for interpretability:
- Coordinate with research institution and business unit to build a business interpretable deep learning model.
- Re-design the loss function with regularization term to meet the legal requirements.
- Reducing the model inference time by adopting Google’s state-of-the-art nearest neighbor algorithm for referential house searching.
Scalable ETL Pipeline for Credit Loan Marketing Data
2021/8-2022/1
- Responsibility:
- Orchestrate the cross-department coordination of the development process.
- Design a low-code python-SQL interface for the data-analyst of business unit and provide development and CICD guidance.
- Ensure business-logic-extensibility in considerations of future marketing campaign.
- Ensure timely delivery of 7 million customer campaign data under 1day computation criteria.
- Accomplishment:
- Have helped the data-analyst of business unit successfully incorporate new campaign business logic into the production pipeline.
- A comprehensive tutorial to the development interface and CICD toolkits, enabling smooth new feature extension with zero-guidance.
- Significantly speedup the pipeline from 5d to 8h via map-reduce parallelization with multiple Airflow worker nodes.
PyTorch development framework for Industry-Academy Cooperation
2021/4-2022/8
- Goal: Develop Pytorch model development framework to reduce the academy-to-company code migration cost.
- Features:
- Support training, validation, testing, weight/performance inspection, and check-pointing of models with different architecture.
- Fixed experimentation flow for all models with formatted running script and folder structure, to enhance code readability and reproducibility.
- Support lazy evaluation and visualization of data pre-processing, avoid re-generation of data in experimentation scenario.
- Supporting parallel model hyper-parameter tuning via Ray (a general python parallelization framework).
- Tools: Pytorch-lightning, Ray, TensorBoard, pyflow-viz
2D Indoor Positioning System through Android App
2014/1-2015/1
Instructor: Professor Ren-Song Tsay
- Survey on the state-of-the-art indoor positioning technologies and write a project proposal. Investigate the relationship between distance and the amplitude of the Bluetooth signal through experiment. Develop an android app that fuses the signals of Bluetooth and Geo sensor for position estimation.
- Score: A+
RESEARCH
Develop techniques applicable to the music industry and design experiments for proof-of-concept
2015/9-2020/1
Instructor: Professor Homer H. Chen
- Developed a context-based tag propagation method to reduce the training noise and successfully improved a deep music auto-tagging model (SampleCNN) by 25.8% in MAP.
- Enhance the robustness of the tag propagation method and successfully extended the improvement to two other neural networks (CRNN and 2D-CNN) with the increase of improvement reaches 6.5%, 4.9%, and 5.5% (for SampleCNN, CRNN, and 2D-CNN, respectively).
- Responsibility: Build up the experiment acceleration (via GPU) environment on a Linux-based server; Design the model evaluation and training strategy with TensorFlow.
- Conduct error analysis to understand the problem in the training data using Matplotlib and Seaborn. Tune the hyper-parameters during model development. Accelerate the evaluation and data visualization process using ray, a parallel processing package.
Research proposal in music for lab funding from the Ministry of Science and Technology (MOST)
2015/9-2017/10
- Wrote research plans about how lyrics may help to improve the user experience of a music recommendation system.
- Research proposals: 1) Interactive, Context-Aware Music Recommendation and 2) A Unified Framework for Processing and Understanding Heterogeneous Data for Intelligent Recommendation.
EDUCATION
M.S. in Graduate Institute of Communication Engineering, National Taiwan University
2015/9 ~ 2020/1
- Multimedia Signal Processing (A)
- Special Topics on Internet of Things (A): Develop an IOT product and demonstrate its functionality.
- Computational Methods and Tools for Data Science (A): Familiarize myself with basic EDA (exploratory data analysis) techniques including PCA and t-SNE.
B.S. in Electrical Engineering Department, National Tsing Hua University
2011/9 ~ 2015/6
- Programming Classes: Logic Design (A+) / Logic Design Lab (A+) / Embedding System Lab (A+) / Computer Programming Design (A+)
- Math Classes: Calculus I&II (A+/A+) Linear Algebra (A+) / Numerical Analysis (A)
SKILLS
- Programming Skills: Design Patterns、ETL、Image Building、Machine Learning、Parallel Acceleration、Python SDK Design、Refactoring
- Development Environment: Linux, Docker, Oracle-Cloud, Visual Studio Code
- Computer Languages: Python、C++、SQL、JAVA
- Tools for Machine Learning and Data Science: Pytorch、Pytorch-Lightning、CuDF (GPU-accelerating dataframe)、Pandas、Networkx、LightGBM、DeepGraphLibrary
- Tools for Model Serving, Deployment, and Data Pipeline: Airflow、FastAPI、 k8S、PostgresDB、RedisGraph、Neo4j、Ray
For Collaboration: Github、Azure DevOps