Vladimir Suponin
Verified Expert in Engineering
Python 3 Developer
Buenos Aires, Argentina
Toptal member since February 20, 2026
Vladimir is a machine learning engineer with five years of experience building production ML solutions. He has worked across NLP, computer vision, and agentic RAG systems, and now focuses on reliable enterprise AI systems. He builds end-to-end pipelines from data preparation and modeling to deployment and monitoring, partnering with product and engineering teams to deliver measurable business impact. Vladimir enjoys applied ML research and turning ideas into practical outcomes.
Portfolio
Experience
- Machine Learning - 5 years
- Python 3 - 5 years
- Computer Vision - 4 years
- Natural Language Processing (NLP) - 4 years
- PyTorch - 4 years
- Deep Learning - 4 years
- Neural Networks - 4 years
- LangChain - 2 years
Work Experience
Data Scientist
Grid Dynamics
- Developed NLP pipelines and intent routing using LangChain and graph-based orchestration with LangGraph.
- Implemented multi-agent context generation workflows to handle niche queries (product recommendations, retail SOPs, IT support).
- Optimized prompt templates and system instructions, integrating Google Vertex AI models for consistent generation quality.
- Built a RAG platform with hybrid search combining semantic retrieval and structured filtering for higher answer relevance.
- Created an automated LLM-as-a-judge framework to audit inference logs and support continuous production monitoring.
- Built an AI simulation module and interactive multi-turn training system for sales managers using LLM-driven dialogue flows.
- Implemented AI guardrails with query moderation and strict safety settings using NeMo guardrails for safer outputs.
- Designed stateful multi-agent pipelines with LangGraph and LangChain to automatically generate and validate training drills.
Deep Learning Engineer
DeepSound AI
- Developed call-center STT models using Whisper and Wav2Vec2 and validated them on real-world noisy audio.
- Implemented transcript post-processing, including re-scoring, punctuation restoration, and summarization workflows.
- Built VAD, noise, and fatigue classifiers to enrich call analytics and improve quality monitoring signals.
- Implemented object tracking and behavior/interaction recognition from real-world camera footage.
- Implemented time series forecasting and monitoring pipelines to support trend analysis and demand/signal prediction.
- Built end-to-end pipelines covering data engineering, training, evaluation, and iteration across projects.
- Improved ASR accuracy by re-ranking hypotheses with LLM-based scoring and lightweight N-gram language model signals.
- Authored data labeling guidelines and QA checklists, improving annotation consistency for training and evaluation datasets.
- Prototyped and evaluated paper-inspired architectures for audio/CV tasks, selecting best-performing designs for iteration.
Deep Learning Engineer
Lomonosov Moscow State University
- Implemented diffusion-based audio generation and style transfer models for music and speech using PyTorch.
- Developed noise-reduction components for speech signals, improving output clarity and robustness across samples.
- Built reproducible training and evaluation pipelines with preprocessing, experiments, and versioned checkpoints.
- Tuned model architectures and hyperparameters to stabilize training and reduce artifacts in generated waveforms.
- Evaluated model quality using objective audio metrics and iterative listening checks to guide rapid iteration.
- Collaborated with researchers to translate research ideas into working prototypes and ablation experiments.
Project History
Agentic LLM Analytics to SQL
To reduce hallucinations and improve safety, I added deterministic guardrails using SQL parsing/validation (SQLGlot) and blocked destructive operations. I implemented a RAG layer for semantic retrieval over documentation (PDF/HTML) using Vertex AI embeddings and PostgreSQL with Pgvector, enabling context-aware query generation. I added observability for prompt and token tracing and exposed basic metrics for monitoring and iterative improvements.
Education
Bachelor's Degree in Physics
Moscow State University - Moscow, Russia
Skills
Libraries/APIs
Pandas, Scikit-learn, PyTorch, REST APIs, NumPy, Matplotlib, Hugging Face Transformers, XGBoost, Playwright, PyTorch Lightning
Tools
Git, Wav2Vec 2.0, Whisper, You Only Look Once (YOLO), Google Kubernetes Engine (GKE), BigQuery, Terraform
Languages
Python 3, Python, SQL
Frameworks
LangGraph, Alembic
Platforms
Jupyter Notebook, Vertex AI, Docker, ClearML, NVIDIA NeMo, Google Cloud Platform (GCP), Amazon Web Services (AWS), Linux
Paradigms
Azure DevOps, Synthetic Data Generation, Machine-learned Ranking (MLR)
Storage
PostgreSQL, Data Pipelines, Redis
Other
Linear Algebra, Optimization, Machine Learning, Deep Learning, Feature Engineering, Model Evaluation, Data Analysis, Natural Language Processing (NLP), Computer Vision, Retrieval-augmented Generation (RAG), Audio Processing, Neural Networks, AI Model Training, Experimental Design, Speech Recognition, Transformers, Voice Activity Detection (VAD), LangChain, Prompt Engineering, RAG Architecture, Large Language Models (LLMs), Data Science, Fine-tuning, Artificial Intelligence (AI), Agentic RAG Systems, AI Programming, Optical Character Recognition (OCR), AI Architecture, Communication, Statistical Modeling, Signal Processing, Noise Reduction, Librosa, Time Series Analysis, Digital Signal Processing, Time Series Forecasting, Pose Estimation, BERT, Object Tracking, Model Deployment, Experimental Research, Hyperparameter Optimization, FastAPI, Pgvector, AlloyDB, Gemini API, Vector Databases, Generative Artificial Intelligence (GenAI), Debugging, Solution Architecture, Data Engineering, AI Agents, Embedding Models, Semantic Search, LoRa, Open-source LLMs, RAG Pipelines, Agentic AI, API Integration, APIs, Gemini Enterprise, Scalability, AI Chatbots, Chatbots, Web Scraping, Random Forests, Linear Regression, Monitoring, Stakeholder Management, Demand Forecasting, Model Development, Data Labeling, Data Visualization, Video Analysis, Datasets, LLM Fine-tuning, Decision Trees, Documentation, Scalable Vector Databases, LLM Reasoning, Reinforcement Learning, Large Language Model Operations (LLMOps), AI Modeling, Diffusion Models, CI/CD Pipelines, Cloud Architecture, Observability, N-gram Language Models, Text-to-text Transfer Transformer (T5), Data Privacy, Personally Identifiable Information (PII), Bayesian Statistics, Multi-agent Orchestration
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