Hi, I'm Tobias Klein.

Senior MLOps Engineer

Architecting reproducible machine learning pipelines that accelerate business impact in globally distributed environments.

Discover My Innovative Projects.
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Turning Data Pipelines Into Strategic Assets

Seasoned MLOps professional focused on building end-to-end, production-grade pipelines that drive measurable value. Combining advanced model development with DevOps best practices, I excel at designing modular workflows and scalable CI/CD systems (including GitHub Actions) that empower cross-functional teams. My holistic approach to data engineering, ML experimentation (using Hydra and Optuna), artifact management (using DVC and MLflow), and robust feature engineering (via custom transformations) ensures rapid iteration, transparent collaboration, and reliable deployments.


By weaving together DevOps principles with cutting-edge data science, I create resilient, scalable solutions that empower teams, streamline experimentation, and transform strategic insights into sustainable business results.

About

I specialize in Senior MLOps solutions, particularly in designing reproducible pipelines that bridge the gap between data ingestion, feature engineering, hyperparameter tuning, and deployment. Drawing on extensive experience handling streaming sensor data from production facilities worldwide, I take a modular approach rooted in Hydra-based configuration management, MLflow experiment tracking, and DVC data versioning.

As highlighted in my recent articles, my focus on Jinja2-based pipeline automation, transformations-driven feature engineering, and atomic script design allows for reproducible, transparent workflows—ensuring rapid iteration and minimizing risk. Whether developing custom CNNs for analog sensor reading or implementing robust continuous integration using GitHub Actions, I prioritize clarity, collaboration, and maintainability at each stage.

By pairing these technical processes with clear communication for stakeholders, I ensure that even complex solutions remain accessible, transparent, and aligned with real-world needs. Whether driving new product initiatives or supporting mature AI platforms, my guiding principle is always the same: build once, iterate fast, and deploy with confidence.

Latest Projects

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A Comprehensive Look at Feature Engineering in a Modular MLOps Pipeline

Effective feature engineering is central to building high-performing and maintainable machine learning pipelines. This project’s codebase illustrates several practices that keep transformations organized, version-controlled, and consistent across training and inference stages.
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A Comprehensive Look at Logging in a Modular MLOps Pipeline

This article explores how logging is integrated into the pipeline, referencing relevant Python scripts in dependencies/logging_utils, Hydra configs under configs/logging_utils, and how each stage’s logs tie back to DVC and MLflow runs.
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Transformations as the Backbone of a Modular MLOps Pipeline

Poor code organization leads to “pipeline spaghetti,” where data ingestion, cleaning, feature engineering, and modeling code are tangled together. This tangle often arises when code is developed in a linear fashion (for example, in one giant notebook) rather than separated into reusable modules for each pipeline stage. The result is code that is hard to test or reuse.