Data Pipeline Experts in Düsseldorf

• Led end-to-end delivery of ML/AI applications for the QuantistryLab simulation platform, from requirements definition and solution design to development, testing, deployment, and production support, coordinating efforts across back-end, DevOps, and ML teams
• Designed and implemented a compound search system with an open-weights LLM, the Pydantic AI agent framework, and the Model Context Protocol (MCP), exposing it through FastAPI endpoints
• Deployed and configured vLLM-based LLM serving on Kubernetes
• Built CI/CD pipelines and Helm charts to automate deployments across staging and production environments
• Instrumented the search system with OpenTelemetry spans and traces, integrating with the Honeycomb observability platform to monitor component-level latency and functional errors
• Developed a custom evaluation dataset and implemented evaluations for retrieval relevance, answer correctness, tool-call accuracy, and end-to-end task success
• Reduced infrared spectrum prediction time by 83% (from 60 to 10 minutes) by replacing a quantum-mechanical model with a pretrained graph neural network; fine-tuned the model using PyTorch, built and containerized the inference pipeline with Docker, and integrated it into the platform
• Implemented an MLflow model registry with MinIO-backed artifact storage
• Advised sales and science teams on ML/AI applications for prospective client projects, providing technical expertise to shape proposed solutions and prototypes

Technologies: AWS, Hetzner, Kubernetes, Helm, Docker, vLLM, Pydantic AI, MCP, Qwen, Llama 3, MLflow, MinIO, GitHub Actions, Python, FastAPI, OpenTelemetry, Honeycomb, PyTorch, Polars, pytest

• 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.

• Gathered and analyzed requirements in close collaboration with business departments to develop data-driven solutions and scalable PowerBI reporting infrastructures.
• Conducted detailed error analyses and optimized existing semantic and data models to ensure a robust data architecture and high data quality and to improve PowerBI reporting.
• Developed and implemented scalable ETL processes for data preparation and integration.
• Built a wiki for table and view descriptions as well as PowerBI PowerQuery and KPI documentation to ensure a consistent understanding of data structures and key performance indicators.

• AI-Enabled Analytics & Reporting: Lead a 2-person BI team, applying predictive analytics and automation to translate complex business requirements into forward-looking insights, recommendations, and financial reports.
• Advanced Data Visualization: Design and maintain 25+ high-impact Power BI/Tableau dashboards tracking company-wide KPIs and trends, structured to support predictive and automated reporting.
• Web Analytics & Insights: Own GA4 tracking and analysis, driving a 15% increase in conversion insight through deeper customer journey analysis.
• Data Pipeline Optimization: Partner with data engineers to redesign and automate data pipeline workflows, cutting manual reporting time by 20% while safeguarding data accuracy and system uptime.

• Led the design, implementation, and optimisation of SAP Analytics Cloud (SAC) dashboards, stories, and reports for business users.
• Worked closely with business stakeholders to gather and analyse reporting requirements, translating them into technical designs.
• Integrated SAC with SAP BW/4HANA, SAP S/4HANA, SAP HANA Views, and external data sources.
• Developed SAC Planning models and supported planning processes, including input forms, allocations, forecasts, and simulations.
• Configured live and import connections to SAP and non-SAP data sources.
• Conducted performance tuning, security setup, and ensured data accuracy in SAC environments.
• Provided training, documentation, and best practices for SAC adoption across the organization.
• Led incident management, change requests, and enhancements in SAC landscapes.

• Evaluate, design, and implement Power BI solutions for Cat4 reporting, significantly improving reporting efficiency, accuracy, and stakeholder visibility.
• Develop and maintain robust data integration interfaces between Cat4 and Power BI, eliminating manual processing bottlenecks.
• Configure and continuously optimise Power BI reports to deliver sharper management insights and support data-driven decision-making.
• Ensure full GDPR data protection compliance across Power BI reports, proactively identifying and mitigating risks.
• Acquired in-depth product knowledge of CAT4, a globally deployed enterprise platform, enabling effective requirements analysis, solution design, and seamless Power BI integration across client environments.
• Developed and integrated scalable reporting templates within the CAT4 product interface, delivering new product features and enhancing data visualisation, reporting efficiency, and stakeholder visibility across global users.
• Trained and mentored staff on Power BI configuration and best practices, accelerating adoption and embedding a data-driven culture.
• Prepared clear technical documentation and delivered hands-on demonstrations to end users and clients, ensuring smooth adoption and confident use of Power BI solutions integrated with CAT4.
• Researched and recommended licensing strategies for cost-effective and scalable Power BI deployment.

SKILLS - BI & Visualisation: Power BI (Desktop, Service, Report Builder), DAX, Power Query

Project and Product Management Tool: CAT4

• Led large-scale data analysis on high-volume event data, applying statistical modeling and time-series methods to detect weak signals in noisy environments
• Designed and maintained scalable Python data pipelines for real and simulated datasets, optimizing signal detection and background estimation at scale
• Applied advanced statistical inference techniques (likelihood-based modeling, hypothesis testing) to support quantitative decision-making
• Deployed data-processing workflows on HPC clusters using Slurm, enabling parallelized analysis and large-scale batch execution
• Built reproducible, version-controlled data workflows using Python, Bash, and Git to ensure reliability and traceability of results
• Translated complex experimental data into actionable insights, enabling quantitative decision-making

• Led the development of robust, scalable, and high-performance micro-services architecture.
• Migrated legacy monolith systems to Spring Boot with RESTful APIs.
• Designed, implemented, deployed, tested, and optimized scalable backend systems ensuring high availability and performance.
• Maintained and deployed Dockerized applications on Kubernetes integrated into CI/CD pipelines.
• Collaborated with cross-functional teams, including frontend developers, product managers, and designers, to translate business requirements into scalable and efficient backend solutions.
• Mentored team members and contributed meaningful code reviews.