Short bio and expertise
About
I am Sudarshan Koirala, a Senior GenAI Engineer at KONE in Espoo, Finland. My work focuses on enterprise generative AI, MLOps, data platforms, and practical AI systems that move from prototype to production.
Before my current Senior GenAI Engineer role, I worked as a Data Scientist at KONE, building machine learning and data science solutions across industrial and enterprise contexts. Earlier, I worked with Nokia Solutions and Networks on NLP and deep learning projects.
I also run Data Science Basics on YouTube, where I teach AI, data science, LLMs, Databricks, and developer tools in a simple, practical way. I write on Medium and share projects on GitHub.
Check out my Resume and CV here:
Work
I focus on two connected areas:
- Building enterprise AI and ML systems with cloud platforms, LLMs, orchestration, evaluation, observability, and MLOps practices.
- Teaching AI and data science through tutorials, articles, and examples that help developers and analysts apply modern tools without unnecessary complexity.
Leading development of AI/ML-driven generative AI solutions. Designing automated workflows for ML solutions, setting up SRE practices, prototyping with LLMs, and guiding data science teams on MLOps best practices across AWS, Azure OpenAI, Databricks, and enterprise AI platforms.
Built data science and machine learning solutions at KONE before moving into the Senior GenAI Engineer role, applying Python, SQL, cloud tooling, and ML workflows to enterprise use cases.
Worked on NLP-based event discovery from social media feeds using spaCy and pre-trained models, implemented deep neural networks for tabular data, and served as scrum master for a team of 15 trainees.
Creating practical tutorials on AI, data science, Databricks, LLMs, and developer tools, including series on Databricks, LangChain, LlamaIndex, AI coding tools, and modern agent workflows.
Expertise
Enterprise AI
Generative AI systems, RAG, LLM prototyping, production workflows, enterprise evaluation, and applied AI engineering.
Data Platforms
Databricks, Spark, SQL, AWS, Azure, MLflow, orchestration, and cloud-native data science workflows.
MLOps
Automated ML workflows, CI/CD, model tracking, deployment practices, monitoring, reliability, and team enablement.
Education
Practical teaching through YouTube, Medium, and hands-on tutorials for AI, LLMs, and data science tools.
Education
Major: Big Data and Large-Scale Computing. Minor: Machine Learning, Data Science and Artificial Intelligence.
Skills
Contact
For business inquiries: basicsdatascience@gmail.com
For consulting: topmate.io/sudarshan_koirala