A
Arash Nicoomanesh
Generative AI Enginner
Profile
About you
Summary
not added
Total work experience
not added
I can work legally in
Iran}, Islamic Republic Of, United Arab Emirates, European Union
Availability
not added
Current work sector
Information Services(N/A)
Languages
not added
Tehran, Tehrān, Iran
Education
Postgraduate Degree of Mathematics, Computer Science, MS
Tehran, Tehrān, Iran
Work experience
GenAi Tech Advisor, Kaggle
2024 - Present
Workplace preferences
Not added
Ideal job
Desired job title
Generative AI Engineer
Position type
Contract
Travel
25%
Relocation
Yes
Kaggle profile
Followers
1087
Skills
Personal data
For how many years have you been writing code and/or programming?
15 years
For how many years have you used machine learning methods?
10 years
Have you ever published any academic research (papers, preprints, conference proceedings, etc)?
No
Select the title most similar to your current role (or most recent title if retired)
Data Scientist
Select any activities that make up an important part of your role at work: (Select all that apply)
- Analyze, understand and visualize data to influence product or business decisions
- Build prototypes to explore applying machine learning to new areas
- Build and/or run a machine learning service that operationally improves my product or workflows
- Experimentation and iteration to improve existing ML models
- Do research that advances the state of the art of machine learning
Approximately how much money have you spent on machine learning and/or cloud computing services at home or at work in the past 5 years (approximate $USD)?
$1000-$9,999
Approximately how many times have you used a TPU (tensor processing unit)?
More than 25 times
What is the size of the company where you are employed?
250-999 employees
Approximately how many individuals are responsible for data science workloads at your place of business?
20+
Technical skills
Programming languages
Python
R
SQL
C
C++
CUDA
Other
ML algorithms
Linear or Logistic Regression
Decision Trees or Random Forests
Gradient Boosting Machines (xgboost, lightgbm, etc)
Bayesian Approaches
Evolutionary Approaches
Dense Neural Networks (MLPs, etc)
Convolutional Neural Networks
Generative Adversarial Networks
Recurrent Neural Networks
Transformer Networks (BERT, gpt-3, etc)
Autoencoder Networks (DAE, VAE, etc)
Graph Neural Networks
ML frameworks
Scikit-learn
TensorFlow
Keras
PyTorch
Fast.ai
Xgboost
LightGBM
CatBoost
Caret
PyTorch Lightning
Huggingface
Computer Vision Methods
General purpose image/video tools (PIL, cv2, skimage, etc)
Image segmentation methods (U-Net, Mask R-CNN, etc)
Object detection methods (YOLOv6, RetinaNet, etc)
Image classification and other general purpose networks
(VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)
(VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)
Vision transformer networks (ViT, DeiT, BiT, BEiT, Swin, etc)
Generative Networks (GAN, VAE, etc)
Natural Language Processing (NLP) Methods
Transformer language models (GPT-3, BERT, XLnet, etc) - General
Transformer language models - pre-training
Transformer language models - fine-tuning
Transformer language models - reinforcement learning from human feedback
Text Embedding Models (BGE, E5, T5, etc.)
Production-Grade ML
ML System Architecture design (Training, Inference, microservices orchestration)
Deploying new models to production
AB testing
On-policy vs Off-policy model training
Monitoring
Model testing
Handling of incidents in production
Investigation of production incidents and Root Cause Analysis
ML OPS best practices
Feature store architecture
Latency optimization
Distributed training
Distributed inference
Feedback discussions with end users of the ML systems
Industry experience
Finance and Banking
Fraud detection
Credit scoring
Risk management
Customer service automation
Churn prediction
Other use cases
Technology and Information Services
Advancements in algorithms
Cloud computing
Data analytics
Development of new software and hardware solutions
Other use cases
Healthcare and Biotechnology
Drug discovery
Personalized medicine
Patient data analysis
Predictive modeling for patient care
Other use cases
Retail and E-commerce
Personalized shopping experiences
Inventory management
Demand forecasting
Attribution analysis and modelling
Automated customer service
Recommendation systems
Other use cases
Manufacturing and Industrial Automation
Supply chain optimization
Quality control
Automation of manufacturing processes
Other use cases
Automotive and Transportation
Development of autonomous vehicles
Traffic management
Route optimization
Predictive maintenance of vehicles
Other use cases
Energy and Utilities
Energy demand forecasting
Grid management
Other use cases
Education
Personalized learning
Predictive analytics for student performance
Educational content recommendation
AI-generated (e.g. LLM) content detection
Marketing and Advertising
Targeted advertising
Customer segmentation
Sentiment analysis
Market trend analysis
Other use cases
AdTech
CTR / Install Rate prediction
Engagement / LTV Prediction
Government and Public Sector
Public safety
Resource management
Social services
Other use cases
Media and Entertainment
Content recommendation
Audience analytics
Creation of personalized media experiences
Other use cases