LightGBM Experts in Germany

• Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
• Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
• Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
• Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
• Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
• Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
• Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.

Self-directed exploration of agentic AI development methods, taken from idea to a working, publicly usable application

• Built an interactive planning tool for modelling retirement withdrawals and tax strategy using an agentic AI (vibe coding) development approach – demonstrating self-directed investigation of new AI-assisted development methods
• Delivered live, tax-aware spending projections and adjustable user inputs; shipped as a free, install-free browser application built in Python, with attention to usability for non-technical users

• 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.

Python, SQL, PySpark, Databricks, Databricks SQL, Delta Lake, dbt, Azure Data Lake Storage, Azure Machine Learning, Azure DevOps, Power BI, Git, MLflow

• Developed, validated, and optimized predictive analytics and classification models using Python (pandas), SQL, and modern ML frameworks.
• Performed data analysis, feature engineering, model validation, cross-validation, and stability analysis to ensure robust model quality and performance.
• Communicated model assumptions, results, uncertainties, and limitations to business units, management, and technical stakeholders.
• Built scalable data and machine learning workflows in cloud-based analytics environments using Databricks and Microsoft Azure.

• Architecture and implementation of a fully digitalized closing flow for managing securities contracts within the DVAG infrastructure. The platform aims for maximum user-friendliness, modular extensibility and compliant handling of sensitive data.

• Implementation of a reactive UI structure with a focus on user guidance & accessibility.

• Dynamic control of form and closing processes including validation logic.

• Reactive state management via SignalStore (signals + selective effects).

• UX optimization through adaptive components and Playwright-based UI tests.

• Backend modularization to connect existing sales and contract logic.

• API stability and DTO design according to Clean Architecture principles.

• Collaboration with domain teams to define technical contracts and service boundaries.

• Management with GitHub.

• Unit tests with Jest, E2E tests with Playwright.

• Code reviews, CI-integrated test execution, iterative refactorings.

• Ensuring high coverage and UI stability in the closing flow.

• Technologies: Angular 18, RxJS, SignalStore, HTML5, SCSS, Spring Boot, Kotlin, REST, OAuth2, Jest, Playwright, Clean Architecture.

• Performed qualitative and quantitative analysis of extracted findings, categorizing themes, evaluating methodologies, and assessing study quality and reliability.
• Produced research reports and evidence summaries communicating key trends, gaps, and opportunities to academic advisors or cross-functional teams.
• Presented findings through well-structured visualizations, tables, and narrative summaries to support decision-making and guide future research directions.

• 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.

• 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

• Built a CRNN model with VGG16 and BiLSTM backbone for text-based CAPTCHA recognition
• Achieved 9.37% character error rate and 68.36% sequence accuracy on validation data
• Expanded data augmentation pipeline with distortions, noise injection, and clutter to improve robustness
• Conducted detailed error analysis on confusable characters (O, Q, D) and proposed error-specific augmentation
• Tech Stack: Python, TensorFlow/Keras, OpenCV, NumPy, Matplotlib

• Developing an AI-based corrosion detection system for maritime infrastructure as part of the CHAI Research Project.
• Built a binary image classification model to detect corrosion using a dataset of 5,000+ metal surface images.
• Automated data labeling from segmentation masks and designed bounding box generation workflows for individual corrosion areas.
• Conducted data preprocessing, data augmentation, and model evaluation (accuracy, precision, recall, F1-score).
• Collaborated with the research team to integrate computer-vision workflows for corrosion monitoring and dataset enhancement.

• Architected and deployed a production-scale GraphRAG system using Neo4j, embeddings, and multi-hop reasoning over 120M+ nodes, improving answer precision by 32%, reducing hallucinations by 41%, and lowering retrieval latency by 38%.
• Designed and implemented an enterprise agent ecosystem using Model Context Protocol (MCP), exposing internal APIs, databases, and services as secure callable tools for autonomous workflows and system integration.
• Designed and deployed a production LLM-based email routing agent using Microsoft Graph API, MCP, and Azure OpenAI, achieving 96% routing accuracy, reducing manual triage workload by 65%, and decreasing response times from 18 hours to under 4 hours.
• Implemented autonomous agent self-correction pipelines using iterative feedback loops (Ralph Wiggum), enabling reliable error detection, automated remediation, and production-safe execution.
• Developed a multimodal semantic search platform using multimodal LLMs and vector embeddings, enabling semantic discovery across 250k+ image and video assets and improving search recall by 48%.

• Build data pipelines for raw and curated data layers using AWS S3, Glue, Athena, and Lake Formation
• Establish CI/CD using GitHub Actions or GitLab CI with CodePipeline
• Prototype models into demo APIs packaged with Docker, versioned with Git, added basic tests with pytest, and assist deployments on AWS SageMaker Endpoint
• Perform exploratory data analysis and feature engineering with pandas and PySpark; track experiments in MLflow or Weights and Biases
• Design and execute A/B tests to optimize user engagement and drive data-informed decisions