teetertater
Joined 7 years ago
Joined 7 years ago
Hi, I’m Yury: 10+ years of experience delivering end-to-end AI projects on tight deadlines. I help companies plan, prototype, and productionalize machine learning systems, modern data pipelines, and enterprise-grade Agentic systems.
Industry focus:
- Regulated Industries (Healthtech, Energy)
- Manufacturing and logistics (Additive Manufacturing, Metals, Lumber, SCM)
- Hardware and Robotics
Tools/Tech: - Languages: Python, SQL, JavaScript (+ some Java/Scala)
- ML/AI: Computer Vision, forecasting, production LLM agents / GenAI
- ML Engineering: Spark, Databricks, DuckDB, dbt/SQLMesh, Delta Lake/Iceberg, Airflow/Dagster, MS Agent Framework (AutoGen), vector DBs
- Cloud, Infra & Ops: AWS, Azure, Docker, Kubernetes, CI/CD (GitHub Actions), Netlify, Vercel
- Frontend/Backend/Viz: Flask, Django, FastAPI, React, Next.js, Grafana
Portfolio: https://yury.techLinkedIn: https://www.linkedin.com/in/yzhuk/
Contact: yury [at] yury {dot} tech
Hi, I’m Yury, an independent consultant and ex-CTO with 10+ years of experience delivering end-to-end AI projects on tight deadlines. I help companies plan, prototype, and productionalize machine learning systems, modern data pipelines, and enterprise-grade Agentic systems.
I bring 10+ years of experience shipping AI/ML projects in domains like:
- Energy & Meteorology (wind-power generation forecasting, steel-plant energy modeling)
- Manufacturing & Logistics (rail freight optimization, demand forecasting)
- SaaS & Data Platforms (Multi-agent systems, recommendation systems, real-time pipelines)
Tools/Tech: - Languages: Python, SQL, JavaScript (+ some Java/Scala)
- ML/AI: Computer vision, forecasting, transformers, NLP, Kalman/EMOS, production LLM agents
- ML Engineering: Spark, Databricks, DuckDB, dbt/SQLMesh, Delta Lake/Iceberg, Airflow/Dagster, MS Agent Framework (AutoGen), LangGraph, n8n
- Cloud, Infra & Ops: AWS, Azure, Docker, Kubernetes, CI/CD (GitHub Actions), Netlify, Vercel
- Frontend/Backend/Viz: Flask, Django, FastAPI, React, Next.js, Grafana
Portfolio: https://yury.techLinkedIn: https://www.linkedin.com/in/yzhuk/
Contact: yury [at] yury {dot} tech
Services: Data Science & AI R&D, Machine Learning Engineering, Data/Software Consulting, Fractional Tech Leadership
Hi, I’m Yury Zhuk, an independent consultant and ex-CTO with 10+ years of experience delivering end-to-end AI projects on tight deadlines. I help companies plan, prototype, and productionize machine learning systems, modern data pipelines, and LLM agents.
I bring 10+ years of experience shipping AI/ML projects in domains like:
- Energy & Meteorology (wind-power generation forecasting, steel-plant energy modeling)
- Manufacturing & Logistics (rail freight optimization, demand forecasting)
- SaaS & Data Platforms (recommendation systems, real-time pipelines, integrations, rapid MVPs)
Tools/Tech: - Languages: Python, SQL, JavaScript (+ some Java/Scala)
- ML/AI: Computer vision, forecasting, transformers, NLP, Kalman/EMOS, production LLM agents
- ML Engineering: Spark, Databricks, DuckDB, dbt/SQLMesh, Delta Lake/Iceberg, Airflow/Dagster, LangGraph, n8n
- Cloud, Infra & Ops: AWS, Azure, Docker, Kubernetes, CI/CD (GitHub Actions), Netlify, Vercel
- Frontend/Backend/Viz: Flask, Django, FastAPI, React, Next.js, Grafana
Portfolio: https://yury.techLinkedIn: https://www.linkedin.com/in/yzhuk/
Contact: yury [at] yury {dot} tech
But if you're inclined to use it, databricks' setup of spark just saves you an incredible amount of time that you'd normally waste on configuration and wiring infrastructure (storage, compute, pipelines, unified access, VPNs etc). It's expensive and opinionated, but the data engineers you need to deal with spark OOM errors constantly is greater. Also databricks' default configs give you MUCH better performance out of the box than anything DIY and you don't have to fiddle with partitions and super niche config options to get even medium workloads stable
Sent you an email.
* Small, ambitious, technical team (6 ppl)
* Shape the future of our startup: own, architect, and implement
* Help us solve tough problems in data. E.g. how to reconcile many data sources and create an efficient MLOps architecture?
Must be in or willing to relocate to Vienna, Austria. Visa Sponsorships available
Tech Stack: Python, Spark, Databricks, PyTorch, AWS, Time Series Forecasting, Meteorology. (you can add things you like to this list)
Send an email to (name in HN profile)@balun.energy to apply.
We develop forecasting algorithms for wind and solar power production. Looking for passionate, driven, versatile people. Make a tangible impact towards sustainable energy.
* Small, ambitious, technical team (6 ppl)
* Shape the future of our startup: own, architect, and implement
* Help us solve tough problems in data. E.g. how to reconcile many data sources and create an efficient MLOps architecture?
Must be authorized to work in EU and willing to relocate to Austria. Visa Sponsorships available
Tech Stack: Python, Spark, Databricks, PyTorch, AWS, Time Series Forecasting, Meteorology. (you can add things you like to this list)
Send an email to (name in HN profile)@balun.energy to apply.
We're considering it but not sure how it scales when gigabytes of data are involved