Position: Senior NLP Engineer
Category: Data Science
English level: fluent
Years of experience: 11

1.Social Media Question Answering
-Implemented QA to a large set of social media posts
-Engineered prompts of various functionality
-Utilized GPT-3.5 API as a core technology
-Implemented multi-step answering for data-level scaling
-Responsible for high latency and load optimization
2.Topic Classification at Scale
-Implemented a semi-supervised markup sourcing approach utilizing vector models and topic keywords
-Introduced an extensive ~100 topic categorization
-Curated the creation of the corresponding train/eval datasets
-Finetuned a multilabel model at low-resource unbalanced setting (class representatives ranging 50-1000 items)
3.Resource-efficient Sentiment Model Adaptation 
-Performed empirical study to detect outlying client domains
-Implemented dynamic classification pipeline for optimal resources/GPU usage
-Facilitated client-specific model fine-tuning with backwards compatibility
4.Object-oriented Sentiment Detection
-Handled a massive client-sourced dataset of object-oriented sentiment with domain imbalances, client-driven biases, errors
-Facilitated training multiple models in various languages (Slavic, Germanic)
-Introduced specific object-oriented pooling from Transformers output
-Developed semantic functionality requirements evaluation: validation of model object-orientation, post-comment related sentiment
5.Machine Learning Pipeline Framework
Implemented and succeedingly supported:
-Pipelining data acquiring, preprocessing and manipulation
-Training/fine-tuning neural networks in pytorch
-Configurations reproducibility and model persistency
-Data access middle layer
-Textual data comprehensive preprocessing
6.Multi-task Classification Model
-Implemented a training pipeline for joint task training
-Utilized distillation methods for model compression
-Facilitated the use of two datasets without crossing markup
-Resulted in x/2 cost reduction in model deployment
7.Semantic and Pragmatic Classification of Social Media posts
-Developed a state-of-the-art categorization system
-Implemented feature extraction and training a classification model
-Curated the creation of the corresponding dataset
8.Webpage Article Extraction
-Implemented generation of webpage parse schema
-Implemented structural clustering of domain webpages
-Facilitated automatic dataset expansion from low-resource markup
- Social media monitoring and analysis domain - 7+ years
- Big data flows with low latency and high throughput
NLP Domain:
- Product business analytics, research & development, planning, tech leading
- Architecturizing research and development
- Performing a full-cycle of hypothesis → research → proof-of-concept → production development
- Accounting for functional and non-functional requirements
- Training and finetuning models
- Curating dataset creation
- Designing services, libraries and frameworks
- no micro-management
- no tracker policy
- flexible working and office hours
- flexible vacation (remote at best)
- laptop and remote server for resourceful development