Foundation Model Experts in Germany

• Title: Enhancing Syntactic Awareness in Transformer Language Models for Hindi Dependency Parsing
• Investigated syntactic knowledge captured by transformer language models (RoBERTa, XLM-RoBERTa) for Hindi dependency parsing, a morphologically rich and low-resource language.
• Developed structure-aware model variants (Struct_Roberta_hi, Struct_XLMR) by integrating a CNN-based parser network between transformer layers, inspired by the StructFormer architecture.
• Conducted extensive error analysis including label-wise, distance-based, direction-based, sentence length-based, and LVC/Non-LVC evaluations.
• Evaluated models on downstream NLP tasks (NER, POS tagging) using the IndicXTREME benchmark.

• Held direct line management responsibility for a team of 4 Platform Engineers — owning hiring, performance reviews, and career development — while establishing a shared engineering standards framework and coaching culture that accelerated delivery across client engagements.
• Led a team of engineers to architect a cloud-native voice AI system for a major inspection client, enabling 2,500 field inspectors to document work fully hands-free via real-time transcription and AI agents — eliminating manual data entry across 440,000 inspections per month and reducing per-user cost from $9 to $1. Stack: AWS (DynamoDB, S3, Transcribe, CloudFront, API Gateway, Bedrock), ElevenLabs, Claude.
• Led a team of engineers to automate multi-region Kubernetes cluster management for a global SaaS leader, reducing provisioning time from 3 weeks to under a day and eliminating 90% of configuration errors. Stack: EKS, Terragrunt, Python, Bash, ArgoCD.
• Accelerator - Cloud-Agnostic AI Platform: Architected and delivered a cloud-agnostic, Kubernetes-native platform as an accelerator, enabling multi-tenant, enterprise-scale management of self-hosted LLMs with concurrent deployment of multiple base models and dynamic LoRA adapter serving. Designed production infrastructure using open-source tooling (ArgoCD, Karpenter, vLLM, SGLang) with automated model lifecycle management, API security (Keycloak + LiteLLM), and cost-optimized GPU provisioning.

Co-founded an AI-native language learning startup, defining the product vision, AI architecture and technical roadmap. Designed and built the AI and backend stack, including LLM fine-tuning pipelines, custom agentic workflows, and scalable inference infrastructure. First product currently in private beta.

• Introduction to AI and Automation
• The Automation Project
• Make.com Foundations
• Automation Basics: Data Management
• Document Workflows, Troubleshoot, and Automate Reports
• Digression: Data as the Foundation of AI Systems
• Implementing AI Solutions in Practice
• Digression: Large Language Models (LLMs)

• Serve as AI lead for life-science solutions, integrating advanced AI models directly into company workflows and ensuring seamless deployment.
• Design and implement deep learning architectures (PyTorch, Keras) for complex biomedical challenges, including cell segmentation, multimodal omics analysis, and prediction of point clouds.
• Develop and deploy robust LLM-based systems, including RAG architectures and agentic workflows using LangGraph, to facilitate natural-language interaction with complex medical data.
• Lead cross-functional initiatives to apply foundation models and explainable AI (xAI) to clinical and evolutionary algorithms.

Concept and implementation of an AI phone assistant that handles incoming calls around the clock, processes them automatically, and passes them on to the responsible employees - including an analytics dashboard.

• Primary Research:
• Developed a compact (<700M parameters) generative vision-language model for whole slide image (WSI) by refining image tokenisation.
• Established an improved evaluation framework, including a curated question-answering dataset and metric selection.
• In preparation for submission.
• Collaboration:
• Conducting research in digital biomarker discovery in computational pathology (CPath) using AI methods.
• Collaborated on projects with international partners, including the Francis Crick Institute (Molecular biomarker prediction in Clear-cell renal carcinoma), HeCOG Greece (Lynch syndrome identification in colorectal carcinoma and Multimodal survival prediction for Prostate adenocarcinoma) and the National Cancer Center Hospital Japan (HIBIRD).
• The work with the Francis Crick Institute is currently being prepared for submission. The collaborative work in Japan has already been published, and the HeCOG projects are ongoing.
• Consortium:
• Manage inter-institutional collaboration and objectives as the KatherLab representative for the LiSYM Consortium.
• Teaching:
• Conducted online workshop sessions for two years at the Clinicum Digitale, educating physicians and medical students on the fundamentals of AI and Python skills.
• Led a multimodal foundation model workshop at the AI in Cancer Research Summer School in Corfu, organized as part of ESAC.
• Presented a talk on vision-language models at the AI in Medicine Summer School, a collaborative event by EKFZ, GENIAL, the TransformLiver Consortium, and ESAC.

• Developed and optimized LSTM-RNN and Decoder Transformer models to predict vehicle trajectory during target loss events in Adaptive Cruise Control systems, achieving 20% improved predictive accuracy over baseline models.
• Engineered novel data preprocessing pipeline from real road campaign data, processing multi-sensor time series data, generating 300+ training snippets.
• Implemented Bayesian hyperparameter optimization and applied physical constraints to prevent model run-away behavior, resulting in 30% smoother acceleration profiles.
• Extended existing patented technology for AI-assisted ACC function improvements, building upon foundational work to enhance network performance.
• Tools: Python, TensorFlow, Keras, Optuna, CarMaker

• Conceived, built and iterated an applied-AI MVP that turns in-depth audio interviews into structured, long-form narrative outputs across multiple genres (e.g. memoir, institutional knowledge, thematic essays) using agentic orchestration and multi-step reasoning.
• Designed and implemented core workflows in a Next.js-based stack, working with structured representations (JSON and other formats), retrieval-augmented generation and emerging knowledge graph structures to maintain context and consistency over long documents.
• Defined and tested agent behaviors across realistic storytelling scenarios, including ideal user journeys, edge cases and failure modes, with explicit criteria for coherence, factual alignment and user intent satisfaction.
• Currently running targeted user tests with selected partners to validate use cases and inform the next product iterations.

• Designed ML-ready data integration workflows between on-premise systems and cloud platforms (Snowflake, AWS Redshift, Azure), enabling scalable feature engineering and model deployment
• Implemented automated ML pipeline deployment using Python, SQL, and CI/CD tools, reducing model deployment time by 60%
• Developed data transformation logic for master data synchronization across ERP and analytics systems, ensuring data quality for predictive models
• Collaborated with cross-functional teams to translate business requirements into mathematical specifications for ML solutions

• Designed an automated model evaluation pipeline enabling LLMs to inspect each other for alignment issues
• Implemented a RAG system with iterative cross-examination for reliable results
• Automated generation of written summaries and hypotheses to support rapid iteration and hypothesis testing

• Architected end-to-end agentic AI system for automated B2B solar sales with multi-step workflows, including planning, memory, and guardrails
• Led a cross-functional team to deliver a production system on schedule while maintaining compliance
• Utilized knowledge graphs and SQL
• Tech: LangChain, Pydantic AI, OpenAI/Anthropic APIs, FastAPI, Neo4j, GNNs, structured reasoning, relational data, SQL, Pandas, NumPy

• Led the technical implementation of several AI products for companies, including defining the software architecture, leading distributed development teams of ML and software engineers, and coordinating delivery with executives.
• Developed and delivered 5+ production-ready AI products in the areas of machine translation, speech AI, document AI, conversational AI, and AI quality evaluation.
• Built a multilingual machine translation platform with over 550 production-ready models for automated translation of documents and business content in more than 40 languages.
• Built production-ready Conversational AI platforms using self-hosted Large Language Models (Qwen) with RAG pipelines, prompt engineering, tool calling, and secure enterprise deployments for internal knowledge assistants and customer-facing chatbots.
• Developed AI orchestration frameworks for dynamic selection of foundation models and for optimizing the quality, latency, robustness, and cost of production AI systems.
• Developed automated evaluation and monitoring pipelines for continuous quality assessment of Conversational AI systems, speech AI, and Large Language Models.

• Design and implement an end-to-end Siemens NX based pipeline to convert OBJ CAD models into graph representations by applying AI-driven clustering of mesh faces into nodes and face adjacency for edges, streamlining GNN integration
• Generate a large-scale synthetic 3D CAD dataset, annotating parts with few MFCAD-style features to ensure balanced, diverse training data for GNN workflows
• Support the design, training, and evaluation of graph neural network architectures for AI-driven detection and classification of geometric features in 3D CAD shapes, accelerating feature-recognition workflows