29.11.2025 aktualisiert
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Premiumkunde
nicht verfügbarSoftware Engineer for AI and Data Systems
Weinsberg, Deutschland
Weltweit
Bachelor of Science in Engineering Business Information SystemsÜber mich
ML & Cloud Engineer mit Fokus auf MLOps, AWS und Azure. Erfahren in der Produktivsetzung von KI-Modellen sowie im Aufbau von ETL-Pipelines, Data Engineering, Deployment und Monitoring. Über 3 Jahre Erfahrung bei BOSCH Engineering als Software Entwickler für Machine Learning und Data Use Cases.
Skills
Künstliche IntelligenzAmazon Web ServicesAmazon Elastic Compute CloudAmazon S3Data AnalysisComputer VisionAutomatisierungAutomobilindustrieMicrosoft AzureBash ShellSaasCloud ComputingCloud DatabaseCloud-EngineeringCUDADatenbankenContinuous IntegrationData ArchitectureInformation EngineeringETLDatensystemeAbfragesprachenLinuxAmazon DynamodbElasticsearchGithubInfrastrukturPythonPostgresqlMachine LearningMysqlNumpyRole Based Access ControlCloud-ServicesTensorflowServiceentwicklungSQLZeitreihenanalyseTypescriptUnstrukturierte DatenVersionierungWorkflowsParquetAWS CdkDatenverarbeitungKatalogeDatenaufnahmePytorchLarge Language ModelsKostenoptimierungJupyterFastapiMatplotlibContainerisierungData LakePysparkScikit-learnKubernetesNintexMachine Learning OperationsRestful ApisAmazon Simple Queue ServicesTerraformDaten-PipelineServerless ComputingDockerDatabricksProgramming Languages
I'm a Software Engineer for Machine Learning Systems, specialized in ML workflows, data pipelines, and deploying AI in production. My focus is on turning research models into scalable, production-ready cloud services.
Core Expertise
☁️ ML Pipelines & Workflows
I build end-to-end ML pipelines that automate the full lifecycle, from data ingestion and preprocessing to model training, deployment, and monitoring.
- CI/CD for ML (model testing, rollout, versioning)
- PySpark workflow design on Databricks
- Automated feature generation and dataset management
- Monitoring and scaling of deployed models
☁️ Productive AI Systems
I bring AI and ML into production using cloud-native design and modern MLOps practices.
- Event-driven and serverless architectures on AWS and Azure
- Model deployment with AWS Batch, Lambda, and SageMaker
- Containerization (Docker) and IaC (Terraform, AWS CDK)
- Scalable inference pipelines for Computer Vision and LLM-based services
- LLM Applications / Agents with Models from various providers
☁️ Data Engineering & Data Pipelines
Strong background in building data-centric systems for analytics and machine learning.
- ETL workflows for large-scale structured and unstructured data
- Time-series and sensor data processing (e.g. automotive MDF data)
- Metadata search and data cataloging using Elasticsearch and Databricks
- High-performance data transformations in PySpark and SQL
☁️ Cloud Engineering
Coming from a cloud engineering background, I apply cloud-native best practices in all my projects, including RBAC, least privilege, cost optimization, event-driven design, and serverless architectures.
I design systems around native data and ML services on AWS and Azure, often leveraging GPU-enabled compute for large-scale ML workloads.
Cloud certifications: AWS Certified Solutions Architect and AWS Certified Data Engineer.
Technologies
- Languages & Frameworks:
- My main and fluent programming language is Python, but i also write typescript, javascript, java and some C / C++
- PySpark, FastAPI, PyTorch, TensorFlow, scikit-learn
- SQL and other database specific query languages
- Cloud & Infrastructure:
- AWS (Lambda, Batch, S3, Glue, Step Functions, SageMaker, Bedrock, EC2, ECS, SQS...)
- Azure (Functions, Blob Storage, Batch, App Services, Queues, Manages Kubernetes...)
- Databricks (Lakehouse, Delta Lake, job orchestration, Unity Catalog)
- Terraform, Docker, GitHub Actions
- Data & Search:
- Elasticsearch (incl. Elastic Cloud, OpenSearch), Qdrant
- AWS DynamoDB
- Relational SQL DBs (Postgres, Aurora, MySQL)
- AI Agents & LLM Applications:
- RAG pipelines, vector search, autonomous AI agents
- Pydantic-AI, langchain & langgraph
- Workflow Orchestration
- Prefect
- Coiled
- N8N
- Other:
- GitHub, Linux, Bash, REST APIs
- CUDA
- Parquet
Mindset
As a Software Engineer first, I approach AI and Data service development with a focus on clean, maintainable, and production-ready code. My goal is to make AI systems that don’t just work in notebooks — but run reliably in production.
Sprachen
DeutschMutterspracheEnglischverhandlungssicher
Projekthistorie
- Neural Automated Labelling
- Led cloud development of a 3D labelling service at petabyte scale, reducing manual effort by over 90% and halving costs.
- Integrated cutting-edge 4D Computer Vision models from Bosch Corporate Research into a cloud-native MLOps pipeline.
- Python, Microsoft Azure, Computer Vision Models, MLOps
- Engineering Data Lakehouse
- Designed and implemented a scalable Data Lakehouse architecture for ML use cases using Databricks and Delta Lake.
- Developed reusable PySpark-based pipeline components to enable rapid cloud deployment of new use cases.
- Built a Databricks-based Lakehouse for time series engineering data with ingestion pipelines and quality enforcement.
- Developed a high-performance metadata search application using Elasticsearch, enabling sub-second queries across millions of fields.
- Python, Databricks, Data Engineering, Vector Search, PySpark
- Customer Engineer Projects
- Created a scalable AWS Batch pipeline for parsing large volumes of engineering data files.
- Python, AWS, Data Pipelines
- Event Finder
- Designed an AI agent service for autonomous detection and explanation of anomalies in time series sensor data.
- Python, AI Agents, LLMs, AWS
- Developed ML algorithms for automated, AI-based analysis of multivariate time series data.
- Deployed models in a production-grade cloud application on AWS using Python.
- Engineered data pipelines for large-scale automotive measurement data (MDF format) and contributed to building a centralized Data Lake.