Computer Vision Expert

Industry Software from Route to Invoice

In Production

In use at: landscaping company, two home care services

The entire workday, digital: sign-in, route plan on the vehicle, departure checks, photo documentation, time tracking, invoicing. The tablets keep working offline and sync afterward. Records are immutable, making time tracking easier to audit. Runs on a dedicated server in Germany with continuous monitoring and remote updates.

Proof: In daily use at the landscaping company with six vehicles since September 7, 2026.

• React Native on Android tablets
• Python
• SQLite
• Two-way synchronization
• Linux server in Germany
• Remote maintenance

Architecture reviews, legacy modernization, system integration, and AI integration into existing systems.

Responsible for the B2B business of Poland's largest e-commerce platform, including full P&L and product direction.

• Full P&L ownership for the B2B Group, responsible for 7bn PLN (~€1.6bn) in annual GMV
• Initiated and lead the business and product group transformation and strategy change to focus on SME customers, leading to a 100% increase in GMV growth (from 12% to 24% YoY)
• Drove AI-based process automation to 48% of B2B Group processes

Statistical Forecasting Algorithm

• Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
• Migration from R/On-premise to Python/Snowflake
• Productionalization on Snowflake in cooperation with data engineers & DevOps

Monitoring Dashboard

• Data engineering for preparation & provisioning of necessary data/resources on Snowflake
• Development & deployment of a Streamlit dashboard in Snowflake

ML-based Probabilistic Forecasting on Vertex AI

• Development of a ML-based probabilistic forecasting algorithm from scratch
• Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI

Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow

• Built core parts of formae's infrastructure engine and cloud integrations in Go, managing resource lifecycles across cloud and Kubernetes environments.
• Designed and implemented an extensible plugin architecture and SDK for integrating infrastructure providers and resources, including AWS, Kubernetes and Helm.
• Built agent-native infrastructure workflows exposing formae through MCP to tools including Claude Code, Codex and Cursor.
• Worked directly with prospective customers on technical evaluations, implementation design and migration paths from existing infrastructure workflows to formae.
• Helped evolve formae through its open-source, multi-cloud and cloud platform releases, with a focus on reliable infrastructure orchestration and developer experience.

Gronic Ident & Sign

Core technologies: Android, Jetpack Compose, Retrofit, Regula Document Reader, Adobe Sign

Android app on dedicated devices for identifications and electronic signatures at the PoS

Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing

• Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
• Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
• Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.

Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision

• Role: Simulation Engineer
• Environment: Mechanical Engineering / Visual Inspection
• Goals & Implementation: Simulation of photometric-stereo setups to determine the best positions for cameras and light sources for each specific part.
• Business Value: Enabled a low-cost and scalable solution for determining part-specific hardware setups.
• Tech Stack: Python, Blender

Independent engineering practice focused on custom AI systems, production delivery, and fractional technical leadership.

Selected client engagement: Scalutions

• Role: Serve as fractional CTO and hands-on technical lead, responsible for the architecture and agentic infrastructure behind its managed B2B outbound operation.

• Product: Designed and built OutboundLoop, an agentic SDR operating system for research, qualification, personalized outreach, campaign management, human approvals, measurement, and continuous improvement.

• Scope: Own the full system lifecycle—from business processes and agent behavior to context design, model routing, integrations, evaluation, telemetry, reliability, cost control, and production operations.

• Own the software system architecture for laboratory automation products; specify interfaces across software, middleware, hardware and motor control in a regulated IVD environment.
• Led architecture evaluations and proof-of-concepts for integrating AI capabilities (anomaly detection, predictive maintenance) into lab automation under medical-device quality standards.
• Introduced GenAI-assisted development tools across the team, improving productivity and code review quality.
• Communicate architecture decisions to product and project management; coordinate research and improvement projects with system, electronics and external partners.

• Expert-supervised AI systems for scientific reasoning, model evaluation, and research workflows.
• Built the IAS Problem Solver, an orchestrated system for difficult mathematical reasoning; it achieved 84% in one submitted answer set on the Leipzig mathematics benchmark.
• Built a resumable state-machine pipeline for research-grade mathematics benchmark generation: source selection, LLM-agent-based phenomenon discovery, task synthesis, gold-answer and certificate generation and validation, probing, repair, human feedback, and quality gates, targeting tasks that are difficult, natural, verifiable, and cost-effective.
• Current work extends this into budget-aware AI research workflows for real scientific problems with expert review.

Tech stack: Python, OpenAI/OpenRouter-compatible APIs, embeddings, RAG, SQLite.

• Designed and implemented a hybrid recommendation system (content-based + collaborative filtering)
• Built end-to-end ML pipelines including data processing, feature engineering, model training, and evaluation
• Developed RAG-based LLM systems using LangChain and vector databases for semantic search and knowledge retrieval
• Established MLOps workflows with MLflow for experiment tracking, versioning, and deployment readiness
• Implemented deep learning models (computer vision & classification) using PyTorch and TensorFlow

• Designed and developed a multi-tenant SaaS platform enabling organizations to build their own knowledge bases and chat with brand-customized AI assistants (white-label approach with dynamic branding per organization).
• Implemented a scalable RAG architecture with a GPT-4o tool-use loop, hybrid semantic search, and strict tenant isolation at database and search index level.
• Built persistent, project-like chat sessions including a streaming API (SSE), multilingual support, and speech input/output (STT/TTS).
• Delivered the cloud infrastructure as Infrastructure-as-Code, fully automated per-customer CI/CD pipelines, and an onboarding process for new tenants.

Technologies used: Python, FastAPI, Pydantic (v2 noted), Next.js, React, TypeScript, Tailwind CSS, OpenAI / LLMs (GPT-4o), Azure AI Search, Cosmos DB, Azure Blob Storage, Azure Cognitive Services Speech, Azure App Service, Azure Container Registry, Retrieval-Augmented Generation (RAG), Server-Sent Events (SSE), Docker, Terraform, GitHub Actions, REST, OpenID Connect (OIDC), Multi-Tenancy

Detecting and classifying vehicles in images and video for traffic monitoring

• A YOLO + Faster R-CNN model built for real-world traffic and autonomous-vehicle scenarios. Awarded Best Paper Award at St Joseph Engineering College, March 2025.

What it does

• The model takes images or video frames and both localizes and classifies vehicles by type, making it usable for downstream applications such as traffic-flow monitoring or perception in autonomous-vehicle systems.

What I did

• Combined YOLO (for fast detection) with Faster R-CNN (for higher-precision classification), rather than relying on a single architecture, trading off speed and accuracy where each mattered most.
• Achieved 90% mean Average Precision (mAP), evaluated using IoU-based metrics rather than just raw accuracy, to properly reflect localization quality.
• Handled the full data processing and evaluation pipeline in Python using TensorFlow and OpenCV.
• The accompanying paper was awarded the Best Paper Award by the Department of CSE at St Joseph Engineering College.

Tech stack: Python, TensorFlow, OpenCV, YOLO, Faster R-CNN