Supervised by Autodia Head of IT and Technology in collaboration with Autodia Documentation Department and external software solutions partners, contributed to the enrichment of existing software tools used internally by Autodia and the development of data pipelines to automate Organisation’s day-to-day tasks. Assisted Autodia in achieving ISO 27001 and 9001 certifications and worked with Autodia Public Performance Department to contribute in the creation of their first customer-oriented digital Portal. Projects included cloud-based solutions to support music works documentation and metadata enrichment; interconnected systems for data exchange between Autodia’s documentation software, Autodia ERP and their website; optimisation and automation of music works matching and duplicate music work merging based on metadata; concert and live music events detection and extraction from sources (Facebook, More.com, TicketServices, etc.)

Machine Learning Engineer at ZESS

October 1, 2020 - January 1, 2022

Worked closely with the Company’s founders, Bioinformatics Lead Engineer and Data Annotators to develop MVP services for Zess and its clients. Designed and implemented ML pipelines from data needs to annotation tasks, implemented ML-based algorithms, validated results and deployed to the cloud. Projects included an NER-based Parser for recipes (AWS SageMaker Pipelines, ECR, Docker) for production via an AWS endpoint; Knowledge Graph enabled Restaurant Recommendation System using Vaticle/TypeDB; Cocktail HyperGraph and ingredient pairing algorithm based on flavour molecules and co-occurrence.

Machine Learning Engineer at AI TECHNOLOGIES LTD.

June 1, 2019 - April 1, 2020

Implemented, tested and prototyped anomaly detection models for Insider Threat activity; delivered code and documentation. Projects included DeepLog (LSTM Autoencoder-based anomaly detection for system logs) and Loglizer (scalable anomaly detection across log types) with Docker, Flask, Django deployments and productionisation.