Named Entity Recognition Experts in Germany

Designed and developed an Azure-based AI and data platform transforming distributed project documents into searchable, structured knowledge for an enterprise AI assistant. Combined incremental document processing, hybrid keyword/vector retrieval, knowledge graph extraction, and conversational AI for Microsoft Teams and Microsoft 365 Copilot. Implemented Python document-processing pipelines for PDF, Word, Outlook and text files; Azure Cosmos DB lifecycle management; Azure AI Search; Azure AI Foundry and Azure OpenAI; identity-aware retrieval with Microsoft Entra ID; project authorization; a TypeScript Microsoft Agents SDK agent; Pulumi infrastructure; resilient scheduling, retry and diagnostic tooling. Technology stack: Python, TypeScript, FastAPI, Pydantic, Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Cosmos DB, Blob/Table Storage, Container Apps/Jobs, Service Bus, Key Vault, Entra ID, Docker, Pulumi, pytest, Microsoft Teams and Microsoft 365 Copilot.

• Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
• Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.

• 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.

Development of a route planner for incident communication.

• Development of the REST API
• Set up a patch system for maintaining the routing graph
• Expansion of the testing infrastructure
• Performance and memory optimization (JMeter, JFR)

Technologies: Java 21, Spring Boot, JGraphT, Flyway, MapStruct, Caffeine, ShedLock, JMeter, Kubernetes, JFR

• Lead a team of 15 annotators on large-scale computer vision projects for autonomous vehicle systems
• Developed comprehensive annotation guidelines that improved inter-annotator agreement by 35 percent
• Implemented quality control processes that reduced error rates by 42% across all projects
• Collaborated with ML engineers to identify edge cases and improve dataset quality
• Managed annotation projects for Fortune 500 clients, delivering 100% on time

• Delivered 40+ AI projects and 23 strategic consultations for international clients (US, Europe, Middle East), achieving a 98% job success rate and building long-term partnerships.
• Developed and deployed production-grade AI solutions in computer vision, NLP, deep learning, and generative AI (LLMs, RAG pipelines, Stable Diffusion, OCR, chatbots), enabling automation and improving client efficiency by up to 70%.
• Designed and fine-tuned large language models (LLMs), including prompt engineering and integration with enterprise knowledge bases, leading to smarter decision-making and reduced manual effort.
• Built real-time computer vision applications (detection, segmentation, OCR) and integrated them into business systems, significantly enhancing accuracy and scalability.
• Consulted startups and enterprises on AI strategy, architecture, and deployment (cloud & on-premise), accelerating product development and reducing time-to-market.
• Managed complete AI project lifecycles (requirements gathering, solution design, deployment, support) in agile, international, and cross-functional environments, ensuring high-quality delivery.

• Insurance service provider*

Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.

Implementation:

• Architecture and production implementation of an on-premise OCR solution with full data ownership
• Methods for recognizing document structures as the basis for automated further processing
• ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations

Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year

Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL

From Crude to Crafted: Refining Participatory Design Data into Stakeholder-Ready Outcomes

• Architected a production Document AI platform using Retrieval Augmented Generation (RAG) over 1,500+ participatory design artefacts to answer historical project queries with grounded responses.
• Designed LLM evaluation combining RAGAS, custom evaluation metrics and human-in-the-loop (HITL) validation workflows to evaluate factual grounding, response quality, and prompt performance.
• Built a React, TypeScript, and D3.js frontend for interactive exploration of AI-generated insights.
• Implemented input layer LLM safety controls and Guardrails, including PII redaction and foul language filtering.

• Teaching German literature and linguistics

• Architected and deployed LLM based AI agents, RAG pipelines, and vector search solutions for decision support across retail banking department.
• Developed and shipped robust AI pipelines with guardrails, error handling, monitoring, and fallback logic ensuring high reliability outcomes and compliance with data privacy.
• Developed and deployed ML models to identify transactional anomalies, improving fraud detection and risk assessment in high-volume datasets for credit risk modelling.
• Built, evaluated and fine-tuned ML models to generate propensity scores for customers used to drive personalized targeting campaigns for credit cards and personal finance/loan products.
• Developed an NLP pipeline using BERT embeddings and spaCy NER for SMS/email analysis and customer query logs.
• Trained machine learning models using Isolation Forest to classify user behaviour and detect anomalies.
• Extracted, cleaned, enriched and feature engineered datasets from different sources to build feature stores that powered ML model training.
• Led development of dashboards using Power BI, Grafana, and Prometheus to monitor model performances, KPI trends, and marketing metrics.
• Built multi-touch attribution models using logistic regression and time-decay weights to evaluate lead quality.
• Developed scalable ETL pipelines from CRM, T24, SAP, and ERP, supporting millions of monthly transactions.
• Integrated testing and CI/CD workflows for robust data pipeline deployment.

• Conduct analytics on operational datasets using Python and Excel to support performance insights.
• Apply statistical analysis to identify patterns and optimize internal processes.

• Evaluated 45+ AI use cases and developed a prioritization framework for Siemens’ internal AI roadmap.
• Tested internal tools like DRIM, SiemensGPT, Microsoft Copilot; presented evaluation outcomes to stakeholders.
• Identified performance gaps and improved tool adoption by 65%.
• Supported AI knowledge-sharing initiatives, led tool onboarding sessions, and improved team AI literacy.
• Collaborated with engineering, procurement, and digital teams on tool feedback and strategy alignment.

• Fine-tuned a quantized LLaMA model with LoRA, optimizing hyperparameters for domain-specific, large-scale NLP applications.
• Led development of LLM-based hybrid RAG architectures using the LangChain framework for the legal domain, integrating Document Extraction, Vector Search, Speech-to-Text processing, and Prompt Engineering methods using OpenAI APIs.
• Built an LLM-powered translation service combining OpenAI Whisper for transcription with domain-specific translation and prompting to handle sensitive diplomacy terminology.
• Developed and integrated REST APIs with FastAPI and Pydantic for AI models, collaborating with front-end teams to deploy production-ready applications in secure cloud environments.
• Automated LLM workflows with CI/CD pipelines, containerized models using Docker, and deployed to AWS for scalable cloud infrastructure.