MLOps & Data Engineer

• Seasoned MLOps specialist with over five years designing, implementing, and operating production-grade machine-learning pipelines on Azure and Databricks, leveraging Docker, Terraform and CI/CD to automate model registration, monitoring, and scalable deployment in pharma and medical-technology contexts

• Expert data engineer proficient in Python, C++, Scala and SQL, with deep knowledge of Spark and Pandas for large-scale data preparation and feature-engineering, consistently delivering robust pipelines while collaborating closely with cross-functional teams

• Doctorate in Bioinformatics complemented by hands-on expertise in advanced ML methods ensuring reproducible, traceable models and rigorous validation aligned with GxP standards

• Proven track record delivering domain-specific AI solutions, from predictive-maintenance to satellite-image vegetation management in Sweden, and molecular-fingerprint discovery

Project focus: Predictive Maintenance

• Operation of predictive maintenance applications

• Implementation of prototypical ML models as production code

• Design of object-oriented software architecture

• Creation and maintenance of data preparation and feature engineering pipelines

• Registration and provisioning of ML models

• Evaluation and graphical presentation of model performance

• Creation and automated execution of unit, component, and integration tests

• Code checks and deployment

• Migration of existing production solutions from HDInsight to Databricks

• Automated creation of infrastructure components (IaC)

• Error analysis and troubleshooting in production software operation

• Code reviews

Project focus: Power-grid simulations for hydrogen supply networks

• Development of a simulator to analyse physical effects in hydrogen supply grids

• Extension of an existing prototype

• Modelling and algorithmic analysis of exemplary supply networks

• Statistical analysis of time-series data

• Generation of machine-learning models to forecast demand profiles

• Evaluation and graphical presentation of simulation results

• Design of object-oriented software architecture

• Code refactoring and unit-test development

Project focus: Computer Vision for ecological vegetation management using satellite imagery

• Implementation of image pre-processing functionalities

• Investigation of augmentation techniques to optimise model performance

• Development of a custom image-registration algorithm

• Creation and configuration of Azure ML pipelines

• Result validation and problem analysis

• Runtime analysis and optimisation

• Development of unit tests

• Code reviewing

Project focus: Molecular fingerprints and search methods in an enzyme database

• Development of molecular fingerprints and search procedures based on biochemical similarity

• Implementation of new concepts for similarity-based search

• Creation of Python/SQL-based stored procedures integrated with PostgreSQL

• Preparation of validation data sets

• Design, implementation and evaluation of validation experiments

• Documentation of code and concepts

• Migration of code from test to production environment

• Troubleshooting of database setup

Project focus: Image processing module for a microservice-based biotechnology software suite

• Porting of code from Windows to Linux

• Implementation of interfaces for components of the microservice architecture

• Rework and optimisation of image-processing and clustering algorithms concerning runtime and memory efficiency

• Design and implementation of test and validation strategies

• Maintenance and extension of software documentation

• Code reviewing