Resume
Dmitry Ivanov
Engineer: Simulation (CAE) · Production ML/MLOps · Applied AI
My work spans three connected competencies, each backed by shipped results: simulation engineering (14+ years CFD/DEM & HPC, team scaled 2→10, <5% deviation vs field telemetry), production ML/MLOps (SoccerPredictAI — end-to-end platform on Kubernetes), and applied AI (CAE Copilot — traceable LLM/RAG copilot for engineering). They combine into roles from Simulation Lead to ML Engineer to AI-in-CAE hybrid — pick the resume that fits. Currently Head of Simulation Modeling Bureau at Rostselmash.
Location Remote · Open to relocation
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General — Full (~2 pages)
All three competencies: simulation, ML/MLOps, applied AI. Default download.
General — ATS (1 page)
Compact one-pager, same neutral framing.
AI in CAE — ATS (1 page)
AI/ML Engineer for CAE & Digital Twin Systems: copilots, surrogates, digital twins.
CAE / Simulation — ATS (1 page)
Lead CFD/DEM Engineer: simulation, HPC, digital twins, team leadership.
ML / MLOps — ATS (1 page)
ML Engineer: production pipelines, Kubernetes serving, LLM/RAG.
Summary
Engineer with 14+ years across three connected competencies: simulation (CFD/DEM, HPC, digital twins), production machine learning (MLOps, Kubernetes serving), and applied AI (LLM/RAG agents). Led a simulation/HPC team from 2 to 10 engineers and delivered R&D-to-production validation at <5% deviation vs field telemetry; shipped production AI systems end-to-end — CAE Copilot (traceable LangGraph + RAG copilot for engineering) and SoccerPredictAI (full MLOps platform). Verification-first mindset in every domain: models are judged against reality with explicit acceptance criteria, leakage-safe evaluation, and calibration. Open to remote roles and relocation.
Key Achievements
- Built and deployed CAE Copilot: traceable AI copilot for engineering workflows (LangGraph routing, Qdrant RAG with mandatory citations, Pydantic calculation tools, golden Q&A eval); Stage 2 shipped on Kubernetes.
- Led simulation-supported development of the H820 harvester — awarded AGROSALON Golden Star 2024 — with R&D-to-production validation at <5% deviation between model predictions and field telemetry. (Rostselmash)
- Built and deployed SoccerPredictAI — domain-agnostic proof of end-to-end MLOps discipline: 950,000+ matches across 26 years; served via FastAPI + Celery on Kubernetes with <500ms p95 latency.
- Reduced manual data extraction from ~8h/day to <30min/day (95% workload reduction) by deploying an OCR + NLP pipeline processing MSDS documents in 12+ languages. (Chemwatch)
- Established CFD/DEM capability from zero at Gomselmash (2015): trained 3+ engineers, published calibration methodology in CADFEM Review (2020).
Selected Projects
CAE Copilot – Engineering AI Copilot
May 2026 – Present
Live app · Technical docs · API (Swagger) · Portfolio case study
- Engineering AI copilot: LangGraph routing, Qdrant RAG with mandatory citations, Pydantic calculation tools, structured reports.
- CAE docs corpus (9 manuals) ingested with page-aware chunks and metadata filters.
- Golden Q&A eval (14 cases): offline baseline 100% intent / citation / tool accuracy.
- Production layout: separate FastAPI + Streamlit images, multilingual-e5-base baked offline, Helm/k8s.
SoccerPredictAI – Production ML & MLOps Platform
May 2025 – May 2026
Live app · Technical docs · Portfolio case study
- Automated data ingestion for 950,000+ football matches across 26 years from 3 heterogeneous sources (Selenium/Selenoid, CDP XHR interception, REST batch).
- End-to-end MLOps: DVC, Great Expectations data contracts, MLflow registry, nested Optuna trials; leakage-safe walk-forward CV.
- Serving on Kubernetes (FastAPI + Celery/RabbitMQ); GitLab CI/CD with quality gates; SOPS + Age secrets management.
- Observability: 11 custom Prometheus metrics, Evidently drift monitoring, Grafana dashboards.
- Holdout log-loss 1.006, ECE 0.004; 560+ automated tests; full technical documentation at docs.soccer.dmitryivanov.dev.
Professional Experience
Head of Simulation Modeling Bureau | Rostselmash
Dec 2021 – Present | Rostov-on-Don, Russia
- Technical Leadership: Scaled a cross-functional simulation engineering team from 2 to 10 members, establishing engineering culture, simulation model review practices, and standardized computational workflows.
- Simulation Workflow Automation: Optimized data preparation and processing across simulation pipelines with Python automation, reducing manual setup time by 12x; integrated workflows with corporate PLM systems (Teamcenter).
- End-to-End Delivery: Led the full lifecycle of complex computational projects (DEM/CFD, MBD/RBD & 1D system simulation) from R&D to serial production validation, ensuring <5% deviation between model predictions and real-world telemetry; simulation-supported H820 harvester awarded AGROSALON Golden Star 2024.
Data Scientist | Chemwatch
Jan 2021 – Jan 2022 | Part-time · Freelance | Remote, Australia
- Built a production batch pipeline for OCR + multilingual NLP to extract structured chemical data (ingredients, CAS numbers, proportions) from 50,000+ MSDS documents across 12+ languages.
- Designed confidence scoring + human-in-the-loop routing with CAS registry validation; reduced manual work by 95% and cut downstream safety analysis time by 40%.
Lead CFD/DEM Engineer | Gomselmash
Aug 2012 – Dec 2021 | Gomel, Belarus
- HPC & Multiphysics Simulation Domain: built CFD/DEM capability from zero (started 2015), trained 3+ engineers, and delivered simulation-validated machinery projects over 9 years, including structural FEA analyses.
- Published calibration methodology (CADFEM Review, 2020) and established verification workflows linking experiments → calibrated parameters → model predictions.
Research Highlights
Highlighted
Building a digital twin of grain crop material flow
2020 D. N. Ivanov, D. V. Dzhasov, A. N. Vyrsky · CADFEM Review #7 (30) — peer-reviewed industry journal · p. 15 · Article
Flagship publication: digital twin of bulk material flow with DEM calibration against physical tests, test-rig methodology, and validation workflow — directly transferable to production ML (data contracts, calibration, verification vs reality).
Read article (PDF)
Full publication list →
Technical Skills
Simulation & CAE
- DEM: Ansys Rocky, Altair EDEM
- CFD: Ansys Fluent, Siemens STAR-CCM+
- FEA: Ansys Mechanical, MSC Nastran/Patran, SolidWorks Simulation
- MBD & 1D: Ansys Motion, MSC Adams, RecurDyn, Simcenter Amesim, MATLAB/Simulink
- Multiphysics: Ansys Workbench
- CAD: SpaceClaim, Siemens NX, SolidWorks, Autodesk Inventor, PTC Creo, AutoCAD, KOMPAS-3D
- Optimization/DOE: Ansys DesignExplorer, Ansys optiSLang
- Post-processing: Ansys CFD-Post, EnSight
- PLM: Teamcenter, Windchill
- HPC & scripting: Python (pipeline automation, Ansys Rocky API), MathCAD
Machine Learning
- Python
- scikit-learn
- XGBoost
- Optuna
- pandas
- NumPy
- Feature Engineering
- Model Evaluation
MLOps
- MLflow
- DVC
- Great Expectations
- Airflow
- Evidently
- Data Quality
- Production ML
Deployment & Production Engineering
- Docker
- Kubernetes
- Helm
- FastAPI
- Celery
- RabbitMQ
- CI/CD
Applied AI & LLM Systems
- LangGraph
- RAG
- LLM
- Qdrant
- Vector Search
- Pydantic AI Tools
Data Engineering
- PostgreSQL
- Redis
- MinIO
- Parquet
Observability & DevOps
- Prometheus
- Grafana
- GitLab CI/CD
- Monitoring
- Observability
- SOPS
- Age
Education
MSc in Mechanical Engineering — Gomel Technical University P.O. Suhogo, Belarus (2016–2018)
BSc in Mechanical Engineering — Gomel Technical University P.O. Suhogo, Belarus (2007–2012)
Certifications & Courses
- LLM: From Understanding to Product — ODS.ai (2026)
- MLOps and Production in Data Science (2.0/3.0) — ODS.ai (2023/2024)
- Machine Learning and Data Analysis — Yandex / MIPT (2020)
- Introduction to Machine Learning — HSE University (2020)
Languages
English: Professional Working Proficiency (B2)
Russian: Native