Amazon S3 Experts in Berlin

• Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
• Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
• Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
• Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
• Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
• Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
• Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
• Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
• Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
• Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices

• Converts natural language into JQL using RAG and pgvector. Returns structured JSON with a query, chart spec, and plain-text answer. A two-stage router answers general questions without touching the JQL pipeline at all.
• Interchangeable LLM backends: Ollama, vLLM, Groq, Anthropic Claude, AWS Bedrock - switchable at runtime, no code changes. Self-healing JQL: on Jira validation failure, feeds error back to LLM, retries up to 4 times. OCI Vault for secrets. Deployed on Oracle Cloud A1 with GPU inference over Tailscale private network. Open source.

• Independently designed, developed, published, and maintained mobile games for iOS and Android.
• Implemented application architecture, gameplay systems, UI, monetization, analytics, and platform integrations.
• Managed the complete release lifecycle, including testing, store publication, production monitoring, and iterative improvements based on analytics.

• Designed and implemented scalable ETL/ELT pipelines using Python, SQL, dbt, AWS and Informatica to ingest data from sources such as APIs, relational databases, and flat files into Snowflake, reducing pipeline runtime by ~30%.
• Migrated high-volume datasets from on-premises Teradata to Snowflake using AWS services (S3, Glue, Step Functions, IAM), ensuring data consistency and integrity.
• Applied Kimball methodology to design star and snowflake schemas, improving query performance and reducing Snowflake compute costs.
• Implemented automated data quality checks using SQL-based dbt tests and the Great Expectations framework to detect anomalies and enforce data correctness before production loads.
• Orchestrated ETL workflows in Airflow using Python and managed code deployments via Git with CI/CD best practices to increase deployment reliability and maintain pipeline uptime.
• Built interactive Power BI dashboards and curated datasets to enable data-driven decision-making for stakeholders.
• Maintained technical documentation in Confluence for ETL workflows, and led knowledge-sharing sessions for new joiners.

• 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.

• Working on the next iteration of tonbo to make it the most flexible in-process analytical database in the market that scales and is operated with strong availability

• Introduced object scope cache to the remote storage layer to avoid I/O churn

• Working on refactoring WAL to support remote storage

• Taking care of the health of the systems as well as designing the operational story and bringing them to production

• Technologies: LSM, WAL, Arrow, Parquet, Rust

• IT training in theory and practice for IT specialists in application development and system integration

• Opt-in RAG extension over tenant-owned data sources (SharePoint, Confluence) on existing multi-tenant enterprise AI platform
• Architecture: ADRs, solution evaluations, permission strategy, cost modeling
• End-to-end SharePoint and Confluence connectors: OAuth consent flows, token refresh, metadata sync, search integration
• Permission resolution: ACL indexing at ingestion and query-time verification (document-level security)
• Elasticsearch hybrid search (semantic + keyword) with RRF scoring
• RAG chat with context injection and source citations
• Stack: Elastic Cloud, Elasticsearch, Docker, Northflank, Next.js, React, TypeScript, Prisma, Microsoft Graph API, Atlassian REST API, OAuth 2.0

• Assessment of the existing reporting landscape and strategic bundling of needs
• Migration and consolidation of reports to Metabase, connected to ClickHouse as the data foundation
• Building and maintaining data pipelines

Stack: Metabase · ClickHouse · Appsmith · Airflow

• Proceeding what was started in 10/2023, offering data science development skills fulltime to international clients

• Helping companies learn more about their existing (unstructured) data, optimize processes and technical systems, and derive solutions for their problems

• Tools and technology used: Python (sklearn, pandas, numpy, Django, sqlAlchemy, pyTorch), Matlab, Docker, AWS EC2, Lambda, S3, SQL, MySQL, Hadoop & Spark, Machine Learning, DNN, AI, Jira, Confluence, Git, CI/CD, GitLab, Jenkins

• Optimized the data architecture (medallion) to better decouple processing stages and improve transparency and reproducibility
• Ensured technical quality of data processing in Databricks by introducing schema enforcement, data quality checks and a structured data architecture
• Orchestrated pipelines with Azure Data Factory
• Professionalized and automated the development and deployment process by integrating Git and GitHub Actions
• Led the Data Engineering team (3 members) in a functional role
• Conducted workshops to optimize and stabilize the data platform and the development process
• Collected and prioritized new requests, maintained the product backlog
• Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS SharePoint, Mural

• Pioneered first enterprise AI-SAP integration at ALDI SÜD, deploying AI-driven automation within one of retail's largest SAP S/4HANA programs, eliminating 50% of manual pre-cycle validation time and establishing replicable automation framework across 11 countries
• Led end-to-end SAP project lifecycle management for implementations across SAP S/4HANA and Manhattan Systems, supporting 7,300+ ALDI SÜD locations globally across Europe and Australia
• Served as primary executive liaison to C-level stakeholders across 11 countries for strategic SAP transformation programs
• Orchestrated automation, performance, and volume testing for critical releases, maintaining 99.9% system SLA compliance during peak retail periods
• Managed cross-functional international teams of 15+ specialists, delivering projects 20% faster than industry benchmarks
• Standardized SAP processes across 11 countries as part of one of retail's largest SAP implementations
• Directly managed €2M budget with 98% allocation accuracy across 12 concurrent projects
• Reduced SAP S/4HANA migration costs by 18% through strategic vendor contract renegotiations and optimization

• Responsible for command post operations and leadership support