ivan.ulitin Get In Touch

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.

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 IPMech 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, 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

Working proficiency — papers, docs, datasets

Education

Saratov State University — specialist degree, with distinction
2019–2025 · «Mathematical Methods of Information Security»

Job / Research Experience

Sberbank Industry

Sep 2025 — Present
Lead Data Science Expert · Moscow
  • Inference infrastructure for large LLMs: DeepSeek v4, GLM 5.1, Qwen 2.5/3/3.5 — 4B to 400B, incl. MoE.
  • SLURM, multi-node launches on clusters up to 16×H100 + 8×A100.
  • vLLM: custom Docker images, serving parameters, throughput optimization.
  • Infrastructure services for 50–60 researchers: GitLab, ClearML, reverse proxies; GPU allocation across teams.

Orb Intelligence Industry

Sep 2024 — Present
Data Scientist · Data Labeling Manager (remote, California)
  • ELT pipelines: PostgreSQL, S3, ClickHouse; Dagster sensor alerting; deployed on Kubernetes.
  • Labeling team of 8–14 people: hiring, training, task design, ground-truth production.
  • LLM experiments: OpenAI / Gemini / Perplexity APIs, prompt engineering, business-metric impact.
  • Vector search (Qdrant, Milvus): recall@1 = 0.997 · recall@10/30/100 = 0.998.

TapBank Industry

Apr 2024 — Mar 2025
Software Engineer · Data Scientist (remote)
  • Documentation chat-bot: synthetic Q/A, graph & vector DBs (Neo4j, Pinecone, Chroma, FAISS), RAG / RAPTOR / GraphRAG via LangChain; answer-vs-annotator cosine similarity 0.74; deployed on AWS.
  • LLM agent generating API endpoints: zero/few-shot, human evaluation.

IPMech RAS Research

Jun 2022 — Present
Research Engineer (remote, Saratov)
  • Venous-disease classification from photos: ResNet50, ViT, DeiT; Flutter app + Flask server. Published in MDPI Mathematics — “Deep Learning Approaches to Automatic Chronic Venous Disease Classification” (2022).
  • Satellite telemetry classification (auroral kilometric radiation) as multivariate time series: RF, kNN, SVM, CatBoost + feature engineering. Abstracts in RSCI.
  • Continuation: PhD research on multivariate time-series analysis (anomaly detection, spectral CV formulations).

Get In Touch