After nearly a decade in the AI/ML trenches, I’ve seen enough overhyped dashboards, doomed data lakes, and machine learning “strategies” sketched on napkins to know what actually works—and what ends up as a cautionary tale on Slide 32. With 8 years of hands-on experience in everything from time series forecasting and predictive modeling to computer vision and NLP, I’ve had front-row seats to both successful AI deployments and the slow-motion trainwrecks that follow when hype outruns infrastructure.
I've built scalable machine learning systems, wrangled big data pipelines, and done my share of MLOps and backend development—basically, I’ve worn all the hats except the one that says “Let’s just AI our way out of this.” I bring not just technical depth, but hard-earned instincts about where the real problems are hiding and how to solve them before they become expensive LinkedIn posts.
I’m looking for projects that are bold but not delusional—places where smart people want to build real things, not just throw buzzwords at a wall and see what sticks.
Computational intelligence for smart grid’s flexibility - prediction, coordination, and optimal pricing.
Modelling and scientific computing
- Implementing and deploying ML solutions use cases for Nokia Engineering and Services Cloud.
- Implementing DBT pipelines for traceable and reliable data transformations for telecom use cases.
- Devoloping and optimizing MLOps pipelines for continuous training, deployment, and monitoring of ML models.
- Developing scalable a platform for fine tunning and serving large language models (LLM) at large scale.
- Founded and developed LAN4AI, a platform that automates data analytics, enabling users to derive insights through natural language queries and AI-generated visualizations.
- Designed and implemented the entire technology stack, including backend architecture, machine learning pipelines, and deployment systems.
- Built Kubernetes-based infrastructure, automated MLOps workflows, and scalable model serving using tools such as Kafka, Spark, MLflow, and Seldon.
- Integrated monitoring and observability solutions using Prometheus and Grafana to ensure system reliability.
- Developed plug-and-play business use cases, including credit risk assessment, churn prediction, and targeted marketing, facilitating seamless integration for clients.
- Conducted market research, led customer discovery, and iterated the platform based on user feedback.
- Designed the architecture of Tecnotree’s scalable machine learning platform built on Kubernetes.
- Implemented and automated ETL pipelines; introduced real-time data streaming with Kafka and Spark.
- Designed and developed machine learning solutions for telecom use cases, including predictive modeling, LLM applications, and machine vision systems.
- Built robust MLOps pipelines for Tecnotree’s AI/ML projects using Airflow, MLflow, and Seldon.
- Developed monitoring solutions for platform components and ML models using Prometheus and Grafana.
- Introduced graph databases and leveraged Neo4j for graph data science in telecom-specific use cases.
Adaptive data analytics and modelling for flexible power systems.
Advising on AI integration strategies for different industrial consulting projects.
- Designing the AI platform’s framework for the smart water project.
- Participating in the design and implementation of ALGOA PROGRESS project.
- Maintaining Vireum’s healthcare platform and web shops.
- Implemented user data analysis algorithms to track users' performance in VR educational simulations using NLP.
- Implemented an AI-based object recognition algorithm and integrated it with an AR application for e-commerce.
See all publications and impact in Google Scholar