Fine-Tuning Experts in Berlin

• Delivered a new notifications platform implementation built from scratch to replace existing and upcoming services
• Collaborating with other teams to integrate more domains

Tech stack:

• Data: Scala 3, Apache Kafka, Python, Airflow, Astronomer
• BE-FE: TypeScript, NestJS, Java, Spring Boot, Vue
• Dev-ops: AWS, PostgreSQL, Docker, GitHub Actions, Kubernetes, Helm, ArgoCD

Designed and built an end-to-end interactive decision-support application using public European data across 27 EU countries and multiple strategic dimensions. Developed a structured analytical methodology for comparing countries, identifying patterns and trade-offs, and exploring strategic choices rather than presenting static dashboards. Translated complex multidimensional data into guided interactive exploration and learning workflows for non-specialist users. Built the application end-to-end using Python and Streamlit, with AI-assisted development and Git-based version control. Developed the project independently from problem framing and data analysis through methodology, UX logic, implementation and deployment.

Tools: Python, Streamlit, Git, AI-assisted development

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• 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.

• Delivered AI and data science solutions, including LLM-based chatbots and data pipelines, improving operational efficiency.
• Collaborated on product features, achieving measurable impact and maintaining strong client relationships.

• RLHF & Model Evaluation: Evaluated, ranked, and refined Large Language Model (LLM) responses based on accuracy, reasoning quality, safety guidelines, and factual correctness.
• Data Annotation & Prompt Engineering: Created high-complexity prompts, edge-case scenarios, and gold-standard reference answers to train and fine-tune generative AI models.
• Quality Assurance & Verification: Conducted rigorous fact-checking, logical consistency validation, and multi-turn response optimization.

• Full automation of the software development process: ticket analysis → AI coding agents → pull request → automated code review → deployment
• Multi-tenant architecture with 82 database models and real-time WebSocket monitoring
• Integration of 40+ AI tools with Claude & GPT
• Tech stack: React, TypeScript, Express.js, PostgreSQL, Redis, BullMQ
• Platform in productive use with paying customers

• Designed a compact framework to generate prompt–response sets for CRM lifecycle scenarios (onboarding, activation, retention, reactivation).
• Included adversarial variants (ambiguous requests, conflicting instructions, policy traps).
• Created a scoring rubric for factuality, tone, and coherence.
• Developed a lightweight guideline for annotator alignment and disagreement resolution.

• Attempted founding of a platform for career development and personal coaching
• Top 3 placement in the Berlin-Brandenburg business plan competition
• Conducted independent market analysis and user research
• Built a comprehensive knowledge graph for roles, skills, and experiences
• Data transformation and setting up data pipelines on Azure

• Designed and developed image classification models for computer vision tasks, including dataset creation, preprocessing, validation, and visualization; applied machine learning and deep learning techniques to optimize accuracy and performance.
• Conducted experiments with appropriate ML algorithms, LLM-based approaches, and tools; performed statistical analysis, hyperparameter tuning, and fine-tuning using test results for robust model performance.
• Developed and optimized CRUD operation APIs using Node.js (Fastify) and Flask (Python), improving latency and ensuring scalability.
• Implemented backend solutions with SQLAlchemy for database management, and automated workflows using cron jobs and AWS Lambda functions.
• Experienced in implementing and maintaining CI/CD pipelines to streamline deployments and ensure reliable software delivery.
• Consistently achieved service time and quality targets while maintaining strong, collaborative, and professional working relationships across teams.

• Defined and executed the end-to-end vision for Allride’s core mobility product, launching a fully functional mobile app on iOS and Android in under 3 months.
• Led product from 0 to 1 crafting roadmap, aligning teams, and driving execution to meet user needs and business objectives.
• Built a tiered S/M/L/XL subscription model based on mobility usage, launching the first MVP with built-in rewards and coupons to drive adoption of Allride’s recurring plans.
• Designed and implemented a high-conversion referral program that rewarded both referrers and invitees, driving 12% of new user acquisition through organic growth loops.
• Developed and launched Allride for Work, a B2B mobility benefit solution that enabled companies to offer sustainable commute plans to employees, driving corporate adoption and unlocking a new recurring revenue stream.
• Aligned product initiatives with sustainable mobility goals, contributing to rapid growth and early media coverage.

• Designed and implemented a real-time perception pipeline using YOLOv7 on Time-of-Flight (ToF) sensor data, enabling live streaming, inference, and on-frame visualization for passenger detection.
• Fine-tuned and evaluated multiple state-of-the-art monocular depth estimation models for Automatic Passenger Counting (APC), and developed a custom hybrid depth model that improved depth accuracy in challenging scene regions.
• Demonstrated that model-generated depth maps outperform raw sensor depth for APC tasks across several datasets, contributing to measurable reductions in counting error.

• 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.

• Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search; cut research time by 30%.
• Designed custom retrieval workflows with LlamaIndex, building a ReAct-style agent for dynamic chunking; improved query accuracy by 18%.
• Researched and optimized embedding strategies, reducing retrieval cost/query by 15%.
• Developed RAG evaluation frameworks using RAGAS and Langsmith with custom datasets; improved coverage by 40%.
• Fine-tuned LLMs (LLaMA 2 on Vertex AI with custom inference containers, dynamic batching, and quantization); reduced inference latency by 25%.
• Integrated AI agents in LangGraph with short-term & long-term memory (Mem0); increased task completion rate by 20%.
• Created schema-aware synthetic data generators; fine-tuned downstream models achieving +12% F1 score.

• Built a hybrid semantic and keyword search and LLM-based requirement extraction from conversational queries, boosting search accuracy by 85%, cutting zero-result searches by 70%, and reducing search time by 60%.
• Deployed a production-ready API with monitoring dashboards over 100K+ products, keeping response times under 2s and reducing customer search-to-purchase time by 40%.
• Technologies: Python, BGE-M3, Qwen2.5, FastAPI, Qdrant, Meilisearch, Docker, Prometheus, vLLM.