Anzor Gozalishvili
Verified Expert in Engineering
Machine Learning Developer
Tbilisi, Georgia
Toptal member since November 17, 2021
Anzor is a senior machine learning and MLOps engineer with 8+ years of experience building production ML systems, data pipelines, and cloud infrastructure. He has designed ML platforms with Dagster, W&B, MLflow, Spark, AWS, and Azure and built agentic systems using LangGraph, RAG, vector search, and MCP. His background spans NLP, document intelligence, deep learning, bioinformatics, and large-scale model training. He is also a published researcher in NLP and computational biology.
Portfolio
Experience
- Docker - 4 years
- PyTorch - 3 years
- SpaCy - 3 years
- Amazon EC2 - 3 years
- Amazon S3 (AWS S3) - 3 years
- Machine Learning Operations (MLOps) - 2 years
- Amazon SageMaker - 2 years
- Amazon SageMaker Pipelines - 1 year
Preferred Environment
Docker, PyTorch, Linux, Amazon Web Services (AWS), Python, Kubernetes, Dagster, Spark, WandB, LangGraph
The most amazing...
...MLOps platform unifying drug-discovery ML pipelines with Dagster, S3 artifact versioning, and W&B experiment lineage.
Work Experience
Senior MLOps Engineer
Self-employed
- Designed and implemented a Dagster-based MLOps platform for drug-discovery ML, adding reproducible orchestration, S3 artifact versioning, experiment lineage, and a custom W&B integration.
- Built a LangGraph-based internal agent for scientists, integrating internal databases, Amazon S3 Vectors, scientific code execution, GPT models, and reusable MCP tools.
- Supported migration of GPU training workloads to AWS ParallelCluster and Slurm, containerizing jobs with Docker, profiling PyTorch/CUDA performance, and benchmarking GPU and CPU instances.
- Partnered with bioinformaticians to translate complex NGS and potency model requirements into technical specifications, aligning R&D drug-discovery objectives with engineering execution for specialized workflows.
- Led the unification of scientific ML pipelines into a shared framework covering ingestion, normalization, feature extraction, training, evaluation, and reporting.
Senior ML Engineer
KYROS Insights
- Designed and optimized multi-target regression models in PyTorch Lightning for large tabular time-series datasets, including custom output heads, loss functions, and Gaussian mixture approaches.
- Reworked the Spark-to-GPU data path using pre-batching and memory-mapped tensors, eliminating batch-construction bottlenecks and reducing training runs from hours to minutes.
- Used PyTorch Profiler and CUDA-level runtime analysis to diagnose training bottlenecks and improve end-to-end model development performance.
- Built stakeholder-facing analysis in Plotly Dash and used MLflow to track experiments, compare model variants, and support iterative development.
NLP Expert | Data Scientist
Pfizer - Manufacturing Operations Solutions
- Led development of a document-processing pipeline combining OCR and computer vision models to classify content and normalize heterogeneous SOP files into a common web-document format.
- Designed extraction strategies for tables, charts, multi-column text, formulas, and other mixed-layout content across heterogeneous SOP documents.
- Evaluated document layout analysis and OCR approaches and integrated multiple models into a production-oriented processing workflow.
NLP Data Scientist
Holocene GmbH
- Built document-processing pipelines for shipping records that extracted structured values from OCR output and combined rule-based logic with fine-tuned NLP models.
- Developed extraction pipelines using LayoutLMv2, Amazon Textract Forms, and rule-based methods, with entity classification layered on top of OCR outputs.
- Added validation logic to reconcile extracted totals and figures against calculated values, then exposed structured results to the core platform through REST APIs.
Senior Machine Learning Researcher
MaxinAI
- Reproduced and extended a bitrate-ladder prediction pipeline, building batch FFmpeg workflows, extracting frame-level features, storing processed data in S3, and training classical ML models.
- Improved bitrate-ladder prediction performance by 3% over the reproduced baseline through new video-derived features.
- Experimented with deep learning approaches for extracting video features directly while reducing inference time toward production constraints.
Data Scientist
Delivery Hero (Outstaffed from MaxinAI)
- Refactored a data pipeline to eliminate a weekly BigQuery-to-Redshift transfer, moving downstream SQL-based data preparation directly to Google BigQuery.
- Engineered new features for a time-series customer retention model, improving model performance by approximately 2%.
- Migrated existing machine learning pipelines to Airflow DAGs, improving workflow automation and reducing manual execution.
- Ran targeted experiments on existing ML workflows using Amazon SageMaker.
Lecturer | Teaching Assistant
MaxinAI
- Delivered lectures and hands-on workshops in machine learning and natural language processing.
- Designed practical exercises, workshop materials, and examples to support applied understanding of ML and NLP concepts.
- Supported students during technical sessions by reviewing implementations, answering questions, and helping troubleshoot modeling issues.
Lead Machine Learning and NLP Engineer
MaxinAI
- Headed MaxinAI’s NLP chapter, working hands-on with a team of four ML engineers across multiple client projects.
- Owned the NLP and OCR pipeline for a Swiss food-regulatory product, including OCR benchmarking, table extraction, text analysis, and structured value extraction from food labels.
- Built a startup data collection and matching pipeline using web crawling, vector search, keyword, fuzzy, and semantic matching for job-candidate recommendations.
- Built a document intelligence product combining multi-vendor OCR, BERT-based question answering, normalization logic, and a feedback loop that converted user corrections into training data.
- Developed deep learning models for merchant-name extraction from bank transactions using LSTM-CRF and CNN architectures, achieving 97% F1.
- Built a semantic search engine for US legal cases using BERT embeddings, keyword extraction, and fast vector search to retrieve relevant passages.
- Built a hybrid food recommender for MenuMend using content-based ranking, collaborative filtering, menu entity extraction, keyword search, and FAISS vector retrieval, with strategies for popularity and cold-start cases.
Experience
ExtractHD Data Extraction Service
SGS Digicomply LabelWise | Food Label Data Extraction Service
https://www.digicomply.com/label-content-managementEye Color Prediction
I optimized the eye color labeling process using clustering approaches to achieve higher classification accuracy. I also examined DNA methylation values at single-nucleotide polymorphisms (SNPs) to understand the gene expression mechanism.
Published the scientific paper.
Georgian LLM Corpus (ACL Datasets)
Syntactic Annotation of Georgian in the UD Schemes (Springer Nature)
Comparative Analysis of Genetic Perturbation Models
IOAI AI Training Program — NLP Lecturer
https://www.youtube.com/watch?v=XNxX7xQZAuM&list=PL5PFQITWvGrdssEHIIe4CS7CWHNnSEXp9Education
Master's Degree in Computer Science
Tbilisi State University - Tbilisi, Georgia
Erasmus Exchange and Scholarship in Computer Science
Universitat Politecnica de Valencia (UPV) - Valencia, Span
Bachelor's Degree in Computer Science
Tbilisi State University - Tbilisi, Georgia
Certifications
Building AI Agents and Agentic Workflows
IBM
Getting Started with AWS Generative AI for Developers
Coursera
Machine Learning Modeling Pipelines in Production
Coursera
Machine Learning Data Lifecycle in Production
Coursera
Deep Learning Specialization
Coursera
Machine Learning
Coursera
Skills
Libraries/APIs
Pandas, Scikit-learn, NumPy, PyTorch, SpaCy, Matplotlib, Natural Language Toolkit (NLTK), TensorFlow, OpenCV, SciPy, FFmpeg, PIL, Keras, Flask-RESTful, Amazon Rekognition, PyMongo, LSTM, Google Vision API, XGBoost, REST APIs, Hugging Face Transformers, PyTorch Lightning, Spark ML, Stanford NLP
Tools
PyCharm, Docker Compose, Gensim, Amazon SageMaker, AutoML, RabbitMQ, BigQuery, Apache Airflow, Scikit-image, Seaborn, ABBYY, Amazon Textract, Pytest, GitHub, AWS IAM, Terraform, Elastic, ELK (Elastic Stack), Spark SQL, Claude Code, Claude, Amazon Elastic Container Registry (ECR), Amazon Elastic Container Service (ECS), Plotly
Languages
Python, SQL, Markdown
Platforms
Jupyter Notebook, Docker, Linux, Amazon EC2, Amazon Web Services (AWS), Databricks, Azure, AWS Lambda, CrewAI, Kubernetes
Frameworks
Flask, LangGraph, Selenium, Scrapy, AutoGen, Agentic Frameworks, Spark
Paradigms
Agile, Testing, ETL, Model Context Protocol (MCP)
Storage
Amazon S3 (AWS S3), Datadog, Elasticsearch, MongoDB, Cloud Deployment, Data Pipelines, PostgreSQL, Neo4j, Graph Databases
Industry Expertise
Bioinformatics
Other
Natural Language Processing (NLP), Machine Learning, Word2Vec, Data Science, Generative Pre-trained Transformers (GPT), MLflow, Deep Learning, Feature Engineering, Machine Learning Operations (MLOps), Sentiment Analysis, Explainable Artificial Intelligence (XAI), Optical Character Recognition (OCR), Text Detection, Statistics, Artificial Intelligence (AI), Classification, Regression, Amazon SageMaker Pipelines, Generative Artificial Intelligence (GenAI), Amazon Redshift, Pipelines, fastText, Recommendation Systems, Information Retrieval, Active Learning, Variational Autoencoders (VAEs), Time Series Analysis, Principal Component Analysis (PCA), Lecturing, Workshop Facilitation, Computer Vision, Video Encoding, Research, BERT, Data Engineering, Bayesian Statistics, Evaluation, Text Classification, Entity Extraction, Data Analysis, Algorithms, Big Data, CI/CD Pipelines, Containerization, Tesseract, Statistical Data Analysis, Time Series, Neural Networks, Recurrent Neural Networks (RNNs), Data Analytics, Algorithmic Trading, Clustering, Experimental Design, Optimization, Genomics, Biology, Clustering Algorithms, Data Visualization, Hugging Face, Transformers, Text Mining, Authentication, APIs, LayoutLMv2, Parsers, Proof of Concept (POC), Profiling, Model Evaluation, Dagster, LangChain, AI Agents, DataTrove, Web Crawlers, Open-source LLMs, Tokenization, Computational Linguistics, Conference Speaking, Data Labeling, Scanpy, Prediction Markets, Predictive Modeling, AI Tools, Large Language Models (LLMs), Large Language Model Operations (LLMOps), Retrieval-augmented Generation (RAG), Vector Search, FastAPI, Agentic AI, Azure Databricks, RAG Pipelines, LLM Reasoning, Knowledge Graphs, Data Transformation, Amazon Bedrock AgentCore, BeeAi, Agentic AI Systems, LLM Integration, Vector Databases, Technical Training, WandB
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