Databricks Experts in Munich

Michael Nelz

Senior ML Engineer | AI Engineer | Problem Solver

Eichenau

Last position:

Senior ML Engineer, AI Engineer at Lanxess AG

Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.

Philipp Grunert

Machine Learning & Data Engineer

München

Last position:

Data Scientist & ML Engineer at Data-Science Factory GmbH

Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
Implementation of automated end-to-end cloud processes
Development of LLM and NLP models
Creation of interactive reports
Support for national and international large corporations as well as medium-sized companies in implementing ML projects

Mirza Klimenta

Agentic AI for a DeepResearch project

München

Last position:

Agentic AI for a DeepResearch project at Freelance

Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
Used multiple experts (OpenAI models) collaborating during document drafting
Extracted useful information from the knowledge graph
Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
Deployed initial application as a Streamlit app

Thomas Hoefkens

Senior MLOps, DevOps Engineer

Last position:

Senior MLOps, DevOps Engineer at Trianel Energy

Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
Integration of RESTHeart to create a REST API for MongoDB.
Build an Angular frontend to simplify data queries and master data maintenance.
Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).

Suyash Shaha

Business Data Science Intern

Last position:

Data Analyst - Reporting & Analytics at SIXT SE

Developed & maintained customer analytical reporting solutions to identify revenue trends, performance drivers, risks & optimization opportunities to ensure data driven decision making across Sales, Finance, Product, Data Engineering & Controlling.
Defined & analyzed customer trends & performance metrics to identify root causes behind variances, anomalies & emerging risks across business domains to deliver actionable recommendations.
Developed & owned analytical data models & reporting layers to ensure scalability, performance & analytical robustness to support executive & operational reporting across business domains.
Planned, tracked & executed projects by ensuring adherence to timelines, data accuracy, consistency, deliverables, reliability & data quality standards through rigorous validation & reconciliation processes.
Raised the analytical maturity by formalizing analytical workflows, documenting data processes & standard operating procedures (SOPs) & conducting training sessions to drive adoption of self-service analytics & embed a data driven culture across operational and business teams.
Took ownership of the end-to-end lifecycle roadmap from requirement gathering, collection, transformation, developing robust business logics to data storytelling & stakeholder delivery.
Converted complexity into structured clarity by translating requirements & business processes into analytical recommendations to ensure alignment between non-technical & technical stakeholders.
Conducted advanced SQL based analysis of complex business datasets to uncover trends, correlations & performance improvement opportunities.
Drove process automation & efficiency improvements by leveraging Python, SQL optimization & AI assisted tools to reduce processing time & increase reliability across analytical & operational workflows.
Standardized KPI definitions & reporting logic to ensure consistency & trust across reporting solutions.
Developed process monitoring dashboards & analyses to identify inefficiencies, bottlenecks & compliance deviations across end-to-end business processes to derive actionable recommendations for process improvement & automation.

Serge Kalinin

MLOps (machine learning operations)

Last position:

MLOps (machine learning operations) at REWE Digital GmbH

It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment

Michael Ternes

Senior DWH Developer

Last position:

ETL Developer at Insurance service provider

DWH for customer and financial data

Extension of the DWH with new data sources
Report development
Data quality management

Methodology: Scrum

Tools: Atlassian Confluence & Jira

Databases: Microsoft SQL Server

Programming languages: SQL, T-SQL

ETL: Microsoft SQL Server Integration Services (SSIS)

Frontend platform: PowerBI, Microsoft Reporting Services

Manikanta Rangaswamy

Data Engineer

Germering

Last position:

Data Engineer at Insurance client

Design, development, and maintenance of end-to-end ETL pipelines for scalable and reliable data integration
Support in data quality checks, testing, and migrations
Development and maintenance of dbt models for structured, modular, and reusable data transformations
Use of AI-driven development to improve ETL job creation and code quality.
Development of CI/CD for automated deployment.

Hardeep Bhutter

Sr. Data Engineer

Last position:

Sr. Data Engineer at Charles Schwab Bank

Designed and implemented end-to-end data pipelines (batch & streaming) using Python, SQL, and Apache Spark, Databricks on AWS reducing ETL latency by 40%.
Developed serverless event-driven ingestion pipelines using AWS Lambda and SQS, ensuring real-time data availability for downstream analytics.
Leveraged Google Cloud Platform (GCP) services including BigQuery and Dataflow to manage cross-cloud data warehousing and analytics integration.
Expertise in DMS (CDC, Full Load) and Airflow for scalable data pipeline automation and orchestration.
Managed and customized data pipelines using Databricks, Airflow. Automation using Docker, Kubernetes, Terraform.
Automated data quality checks using dbt to modularize transformations and ensure production-grade data lineage, improving reliability by 30%.
Collaborated with compliance teams to ensure GDPR and SOC2 alignment. Mentored junior engineers and contributed to architecture refactoring for scalability.
Created and maintained dashboards in Power BI to provide actionable insights.

Harald Laschitz

Project Management

Grünwald

Last position:

Project Management at Loocid LLC

Conducted a comprehensive due diligence review for a potential acquisition of a Swiss manufacturing company as part of a pre-merger analysis
Assessed production capacities and technical infrastructure
Evaluated integration possibilities into existing business processes
Performed risk assessment and developed recommendations for action
Documented the findings and presented them to management

Axel Kraus

Data Engineer & Business Analyst

Last position:

Data Engineer & Business Analyst at Metafinanz

Migration of existing data jobs from Cognos Data Manager to Tibco/IBI Datamigrator
Migration data jobs parametrisation for dynamic runs
Optimisation and cutting-back
Regression tests
Knowledge transfer and documentation

Christian Schulz

Data-Scientist/AI Engineer

Ismaning

Last position:

Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG

Concept creation and implementing AI Agents in AWS Cloud
Continuously alignment with stakeholders
Collaborate with DevOps
Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.

Nima Nooshi

Data and AI architect

Last position:

Co founding LLM Engineer at LLM Ventures

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

Patrick Upmann

Interim Manager & Consultant for Data, AI & Regulatory Governance

Grasbrunn

Last position:

Interim Management | Consulting & Implementation | Data Deletion in SAP at BSR (Berliner Stadtreinigung)

Topics: Business Analysis, Data Privacy, Data Management, Stakeholder Management, Conceptualization
This project focuses on developing and implementing a strategic approach for data deletion in SAP systems. The goal is to identify the relevant data and structures during system migration to ensure both data privacy and IT system efficiency. At the same time, downtime should be minimized and regulatory requirements met.
Development of a comprehensive approach for data deletion in SAP systems, considering data privacy and business requirements.
Ensuring efficient and structured data transfer to the new system.
Optimizing system efficiency and reducing downtimes during migration.
Creating functional and technical concepts to ensure compliant and sustainable data management.
Topic preparation: Detailed study of the "data deletion" area to lay the foundation for a structured data migration.
Definition of project structure: Setting roles, interfaces and the project's organizational structure.
Regulatory requirements: Analysis of data privacy regulations and business requirements to define deletion criteria.
Approach: Developing possible scenarios and methods for data cleansing and deletion.
Deletion concepts: Creating functional and technical deletion concepts that structure the implementation and provide clear guidelines.
Setting deletion criteria: Defining which data and structures to delete or transfer.
Responsibilities: Clarifying responsibilities within the project team and among stakeholders.
Analysis of ongoing activities: Identifying and collecting existing activities in the "data deletion" area.
Effort, cost and timeline planning: Creating estimates for resources, effort and budget.
Implementation initiatives: Developing and executing concrete measures to apply the defined deletion strategies.
IT system efficiency: Analyzing the existing IT infrastructure to identify optimization potential for data deletion and transfer.
Technology trends: Evaluating new technologies and tools that can support the data cleansing process.
Cost-benefit analysis: Assessing the financial impact of data cleansing and the introduction of new solution approaches.
Risk management: Identifying potential risks during implementation and developing appropriate mitigation measures.
This project lays the foundation for a sustainable and compliant data transfer to a new SAP system. With a clear approach to data deletion, it meets data privacy requirements, reduces downtimes and increases the efficiency of the new system. The results and recommendations will help companies develop a future-proof data strategy that meets legal and business needs.

Stephan Sahm