Natural Language Processing Experts in Hamburg

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

• Successfully completed a 3.5 month data science course, earning the ‘Certified Data Scientist’ title from XDi, Germany (AZAV certified).
• Covered supervised and unsupervised machine learning algorithms.
• Covered natural language processing using Python.

• Reviewing functional and technical requirements from a testing perspective
• Creating test cases and automated tests to validate requirements
• Performing manual and automated functional, end-to-end, and regression tests
• Documenting test results and tracking defects
• Using models like GPT-4, BERT, and Hugging Face Transformers for automated test case generation, analysis of test results, and improving test coverage, including bias checks and security reviews
• Techs: MS Office, Jira, Zephyr, Confluence, Tosca, stakeholder communication, Agile, Kanban, Scrum, OpenAI API, Hugging Face, PyTorch, LangChain.

Relocation from the UK to Germany and structured transition period focused on consolidating executive experience in data platforms, analytics, and AI, and formalising decision-centric data and AI frameworks, including governance models in preparation for the next long-term leadership role.

• 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.

• Delivered 3 video and multimodal evaluation projects, shipping 80+ high-complexity video with 90% QA pass rate
• Improved annotation workflow and QA gates, reducing average handling time by 15% and rework by 23%

• Attained AWS Certified Cloud Practitioner certification.

• Mastered Rust through self-study, including books, online courses, and open-source contributions.

• Developed a serverless web application using AWS (RDS, Lambda, Polly, Amplify) and TypeScript/React/D3, managed infrastructure with CDK.

• Continuously stayed updated with industry trends through self-education, webinars, and workshops, exploring Data Mesh and FastAPI.

• 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