Kristina N.
MLOps & ML Engineer
My specialisation is building end-to-end AI systems with a focus on production-ready pipelines for Computer Vision, NLP (LLMs) and tabular data. My expertise lies in designing workflows that go beyond model training — including data validation, monitoring, deployment in real-world environments. I try to combine experience in MLOps, data engineering, and data analysis to ensure that machine learning systems are not only accurate but also stable and scalable in production. My strong side is setting up structured pipelines and handle noisy real-world data.
Key Expertise
Experience
6+ years
Timezone
CET (UTC+1)
1. Embedded Computer Vision Pipeline for Edge Device
Project overview:
Developed an end-to-end Computer Vision pipeline to detect light bulb activity on industrial machines and generate operational statistics (working vs idle time). The system enabled automated reporting and provided actionable insights to improve machine efficiency and business decision-making.
Responsibilities:
- Designed and implemented the full ML pipeline: data preprocessing, augmentation, model training, and validation
- Experimented with and evaluated multiple object detection models to achieve stable performance in real-world conditions
- Optimise models for edge deployment (latency, memory footprint, inference constraints)
- Built and deploy inference workflows on embedded camera devices
- Integrated monitoring and validation steps to ensure consistent performance after deployment
Achievements:
Successfully delivered a production-ready pipeline that enabled reliable on-device inference. Reduced manual intervention by structuring the full lifecycle — from training to deployment — into a repeatable and scalable workflow.
2. Image Processing Pipelines for Cosmetic Brands
Project overview:
Worked on pipelines processing large-scale image data provided by cosmetic brands. The system required consistent validation and monitoring of incoming data to ensure quality across multiple datasets used for downstream AI models.
Responsibilities:
- Tested and validated data pipelines handling image ingestion and preprocessing
- Implemented monitoring mechanisms to track data quality across datasets
- Debugged pipeline failures and inconsistencies in incoming data streams
- Investigated edge cases in image data affecting model performance
- Collaborated with data and ML teams to ensure dataset reliability
Achievements:
Improved data consistency and pipeline reliability by introducing structured validation and debugging processes. Helped ensure that downstream models received clean, well-structured data, reducing failure rates and improving overall system stability.
Key Expertise
Experience
6+ years
Timezone
CET (UTC+1)
Ready to Work with Kristina N.?
MLOps & ML Engineer
Share your project details and our team will review the match and confirm availability.
We respond within 24 hours.