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jeffrey rivero
AI reasearcher
Profile
About you
Summary
A seasoned technology leader with over 25 years of experience spanning cybersecurity, artificial intelligence, and software architecture.
Total work experience
15+ yrs
I can work legally in
United States
Availability
Immediately
Current work sector
HR & Recruitment(Training & Development)
Languages
English (US)(First Language)
Davie, Florida, United States of America
Education
3 or 4 yr. Undergraduate degree of Computer Science, Florida Atlantic University
Boca Raton, Florida, United States of America
Work experience
Ai Solution engineer, Check AI Labs
2023 - Present
Workplace preferences
Ideal workplace
Co-workers
Office meetings
Flexible working hours
Speed of change
Work from home policy
Ideal job
Desired job title
AI Solution Engineer
Position type
Contract
Travel
25%
Relocation
No
Kaggle profile
Followers
none yet
Skills
Personal data
For how many years have you been writing code and/or programming?
25 years
For how many years have you used machine learning methods?
7 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)
Machine Learning/ MLops Engineer
Select any activities that make up an important part of your role at work: (Select all that apply)
- Build and/or run the data infrastructure that my business uses for storing, analyzing, and operationalizing data
- 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
- Do research that advances the state of the art of machine learning
- Experimentation and iteration to improve existing ML models
- Analyze, understand and visualize data to influence product or business decisions
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)?
$10,000-$99,999
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?
0-49 employees
Approximately how many individuals are responsible for data science workloads at your place of business?
1-2
Technical skills
Programming languages
Python
SQL
C
C++
Java
Javascript
Bash
C#
PHP
Go
CUDA
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
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
Latency optimization
Distributed training
Distributed inference
Feedback discussions with end users of the ML systems
Industry experience
Finance and Banking
Fraud detection
Risk management
Customer service automation
Technology and Information Services
Advancements in algorithms
Cloud computing
Data analytics
Development of new software and hardware solutions
Manufacturing and Industrial Automation
Predictive maintenance
Telecommunications
Fraud detection
Education
Educational content recommendation
AI-generated (e.g. LLM) content detection
Marketing and Advertising
Sentiment analysis
Cybersecurity
Threat detection
Anomaly detection
Development of secure systems
Other use cases