AI/ML Engineer Consultant at Live Reply

Delivered an enterprise GenAI knowledge-management system over 200+ technical documents, reducing operator search time by 52% through ingestion pipelines, retrieval evaluation, Qdrant, Neo4j, and agentic GraphRAG workflows. Designed an Azure-based agentic analytics platform that converts transactional data into validated, actionable reports, reducing recurring report preparation time by 81% via SQL and object-storage integration, specialized agents, workflow memory, and automated quality checks. Built a stateful multi-agent orchestration platform with Python and LangGraph for non-technical users to automate business processes. Provisioned reproducible AWS environments with Terraform, Kubernetes, networking, load balancing, and AI services. Led GenAI coding-platform adoption across 4 engineering teams by defining agent workflows and reusable tool integrations.

Applied AI Researcher (Short-Term Collaboration) at BeyondShape

Delivered applied-AI pipelines including a 3D spine-reconstruction workflow using gradient boosting, convolutional networks, and generative methods, and an isolation-forest anomaly detector for large-scale ISIC dermatology data.

Applied AI Researcher at University of Tübingen

Built a reproducible PyTorch and SLURM pipeline for distributed medical-image training, inference, experiment tracking, and explainability evaluation across ~70,000 DICOM/NIfTI images and ~300,000 clinical records. Developed generative-AI and explainable-AI methods for psychiatric neuroimaging using diffusion-based counterfactual generation, computer vision, statistical evaluation, and clinically oriented model interpretation. Contributed to open-source medical-AI projects including MedSAMix and related work on neuroimaging explainability.

DevOps Engineer / Telecommunications Consultant at Hewlett Packard Enterprise

Built a Jenkins, Java, Spring, and SQL deployment system for a major telecommunications customer, reducing release execution time by ~90% while improving repeatability and operational reliability. Owned delivery across client, engineering, and operations stakeholders, translating requirements into designs, implementation plans, and production releases. Implemented backend integrations and introduced containerized delivery components, removing production blockers across multiple release cycles.