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Ivan Ulitin

Data Scientist & ML Infrastructure Engineer

Building LLM inference infrastructure — from serving optimization on 16×H100 + 8×A100 clusters to platforms for 50–60 researchers — and applied ML: multivariate time series, computer vision, RAG.

Ivan Ulitin

Core Expertise

LLM Inference & ServingvLLM · SLURMMLOps / InfrastructureVector SearchRAG / LLM AgentsComputer VisionTime-Series AnalysisData Engineering (ELT)Labeling Ops

About Me

I'm a lead data-science expert at Sberbank, where I run inference infrastructure for large language models — DeepSeek, GLM, Qwen, 4B to 400B parameters — on SLURM clusters of 16×H100 + 8×A100, and build tooling used by 50–60 researchers: GitLab, ClearML, LLM gateways, reverse proxies.

Parallel to that: data science at Orb Intelligence (ELT pipelines, labeling ops, vector search at recall@1 = 0.997) and research engineering at the Institute of Precision Mechanics and Control, RAS, where work on satellite telemetry grew into first-year PhD research on multivariate time-series analysis — with a published computer-vision paper on the way.

Outside work: photography and video editing, blogging, powerlifting, playing musical instruments.

Key Skills

Languages

PythonSQLRustJavaScriptDartCypherJavaRuby

ML / Data Science

PyTorchTransformersvLLMLangChainScikit-learnpandasSeaborn

Infrastructure

DockerKubernetesSLURMGitLabClearMLLinuxFastAPIDagster / AirflowS3ClickHouseAWSGCP

Concepts

Data analysisProbability & statisticsMachine learningDeep learningComputer visionNLPTime seriesManagement

Languages

Russian

Native

English

B2

Working proficiency — papers, documentation

Education

Saratov State University — specialist degree, with distinction
2019–2025 · Mathematical Methods of Information Security
PhD studies — 2025–present (first year)
Approximate topic: “Artificial-intelligence methods for multivariate time-series analysis”

Job / Research Experience

Sberbank Industry

Sep 2025 — Present
Lead Data-Science Expert · Moscow
  • LLM inference infrastructure on GPU clusters:
    • deployed DeepSeek v4, GLM 5.1, Qwen 2.5 / 3 / 3.5 — from 4B to 400B parameters, including MoE models;
    • multi-node launches and SLURM job scheduling: initialization, configuration, and running compute jobs on clusters of up to 16×H100 + 8×A100 nodes;
    • operated recent vLLM releases: built Docker images, tuned serving parameters, optimized configurations for higher throughput.
  • Infrastructure services for 50–60 researchers:
    • deployed and maintained GitLab and ClearML;
    • set up reverse proxies and kept internal services available;
    • managed infrastructure and redistributed GPU resources across research teams by task priority and cluster load.

Orb Intelligence Industry

Sep 2024 — Present
Data Scientist · Data-Labeling Manager (remote, California)
  • Data pipelines:
    • piloted Google AlloyDB; optimized the ELT pipeline over PostgreSQL, S3, ClickHouse;
    • built Telegram alerting via Dagster sensors; created assets extending the ELT pipeline;
    • deployed on Kubernetes.
  • Data-labeling management:
    • job-market analysis, vacancy design, test tasks, interviews, labeler training;
    • ran a labeling team of 8–14 people and produced ground-truth datasets.
  • LLM experiments for keyword-based information search:
    • benchmarked open-source LLM agents; OpenAI / Gemini / Perplexity APIs, prompt engineering, LLM pipelines;
    • directed the labeling team to build ground-truth datasets; tracked improvements of business metrics.
  • Vector search:
    • data augmentation for diverse embeddings; Qdrant, Milvus;
    • results: recall@1 = 0.997 · recall@10/30/100 = 0.998.

Institute of Precision Mechanics and Control, RAS Research

Jun 2022 — Present
Research Engineer (remote, Saratov)
  • Mobile app + Telegram bot for venous-disease grading:
    • data crawling (Scrapy, Selenium) and processing;
    • ResNet50 for leg-vs-other photos (binary); ResNet50 and ViT / DeiT transformers as baselines for multiclass severity grading;
    • Flutter (Dart) frontend talking to a Flask Python backend; JMeter load testing, unit tests;
    • co-authored the paper — MDPI Mathematics — and spoke at conferences.
  • Satellite telemetry classification as a multivariate time series (auroral kilometric radiation):
    • satellite-data preprocessing;
    • baseline models — Random Forest, kNN, SVM, CatBoost — plus time-series feature engineering; prediction-quality assessment;
    • thesis published: RSCI.
  • Developed a university course — ML time-series analysis:
    • ARMA, ARIMA, SARIMA and related forecasting algorithms;
    • time-series feature engineering: Fourier transform, shapelets, etc.;
    • neural networks for forecasting, classification, and generation of time series (e.g. LSTM);
    • public course materials: GitHub.
  • Developed a university course — computer vision:
    • classical image preprocessing (Prewitt, Sobel filters, etc.);
    • CNNs and transformers (incl. ViT) for classification, segmentation, and VQA;
    • documentation and study materials for students.

TapBank Industry

Apr 2024 — Mar 2025
Software Engineer · Data Scientist (remote, Moscow)
  • Customer-support chat-bot:
    • preprocessing of heterogeneous documentation (text, code, links, images);
    • synthetic question/answer generation;
    • graph and vector databases: Neo4j, Pinecone, Chroma, FAISS;
    • LangChain pipelines; RAG / RAPTOR / GraphRAG architectures — answer-vs-annotator cosine similarity: 0.74;
    • deployed on AWS.
  • LLM agent for API-endpoint code generation:
    • test sets across design patterns;
    • zero- and few-shot via OpenAI API;
    • human evaluation.

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