AutoGen Experts in Germany

"Reasoning Models: From LLM Foundations to NOC Automation."

• Specialized in designing and implementing robust, secure cloud solutions for critical client infrastructure.
• Expertise in Microsoft Intune environment with a strong focus on system hardening and comprehensive policy management.
• Architected NIST and ISO/IEC 27000 compliant Mobile Device Management (MDM) infrastructure tailored for an international government defense aerospace project.
• Performed an architectural role for an offline Microsoft Endpoint Configuration Manager (MECM) environment, ensuring NIST compliance while handling complex manufacturing infrastructure.

• Automating Software Development with Generative AI (Master Thesis): Architected a gen-AI prototype using agentic workflows, LLMs, and RAG, achieving €100,000 in annual savings by cutting development cycles by 60–80%. Collaborated with cross-functional teams to automate the software lifecycle, boosting operational efficiency by over 60%.
• Development of an AUTOSAR AI-Based Chatbot: Implemented an AI-based chatbot using NLP, vector databases, and RAG pipeline, enhancing documentation retrieval efficiency via REST APIs by 80%. Presented findings to senior management to secure strategic adoption and executive buy-in.
• Automation of Stubbing Processes with Generative AI: Designed a generative AI solution that automated build system processes, cutting manual deployment tasks by 60% and integrating seamlessly with Git workflows.
• Co-Simulation and RTE Standard Evaluation with Gen-AI: Accelerated compliance assessment for the RTE standard by 45% using generative AI tools and Python for evaluation.

• Strategic transition into AI Engineering through intensive mentoring and project execution.

• Developed MASX AI, an agentic AI platform integrating LangGraph, AutoGen, and RAG for geopolitical forecasting and real-time ETL.

• Designed and delivered functional AI prototypes for prospective clients showcasing applied expertise in multi-agent systems, real-time data pipelines, and LLM integrations.

• Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
• Designed and implemented multi-agent AI workflows for financial and trading applications
• Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
• Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
• Led system architecture decisions across model selection, orchestration, state management, and deployment

• 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.