01.09.2025 aktualisiert

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Premiumkunde
100 % verfügbar

Senior Data & AI Engineer & Architect

Münster, Deutschland
Deutschland +9
Bachelor in Business Administration (Finance & Controlling), Master in Quantitative Finance
Münster, Deutschland
Deutschland +9
Bachelor in Business Administration (Finance & Controlling), Master in Quantitative Finance

Profilanlagen

CV - Felix Kleine Bösing

Skills

JavascriptKünstliche IntelligenzAmazon Web ServicesAmazon S3Künstliche Neurale NetzwerkeMicrosoft AzureC++Cloud ComputingComputerprogrammierungData ArchitectureInformation EngineeringIngenieurwesenForecastingGithubApache HadoopApache HiveIdentitätsmanagementInfrastrukturPythonPostgresqlMachine LearningMongodbNatural Language ProcessingRedisSoftwareentwicklungSQLTypescriptScriptingData ScienceApache SparkDeep LearningGenerative AIGitlab-CiKubernetesFull Stack EntwicklungAWS FargateDocker
I can assist you in any phase of a use case. Whether it's the initial conception with the business departments, design of the infrastructure and application architecture, the hands-on implementation, and/or team leadership of the developer team for scaling projects. Here is a non-exhaustive list of possible projects/engagements:
  • Full Stack Development of Data & AI Applications (Dashboards, operational, administrative tools, development of ML models for various use cases)
  • Design and Implementation of Data Platforms
  • Development of Teams for Data Science, Data Engineering, and Machine Learning Engineering (Recruiting, Mentoring, Coaching)
  • Design of Corporate Data Strategies
Skills:
  • Software Engineering
  • Data & AI Architecture
  • Machine Learning, Deep Learning, Reinforcement Learning
  • Timeseries Forecasting
  • Data Science
  • Data Engineering
  • MLOps
  • Natural Language Processing
  • Generative AI
Programming and Scripting Languages:
  • Python
  • SQL
  • Rust
  • C++
  • R
  • Typescript/JavaScript
Engineering Tools/Frameworks:
  • Docker
  • Kubernetes
  • Helm
  • ArgoCD
  • GitLab CI
  • GitHub Actions
  • Postgres
  • MongoDB
  • Redis
  • Hive
  • Spark/Hadoop
Cloud:
  • AWS (EKS, Fargate, S3, RDS, IAM, Step Functions, Ingocnito)
  • Azure (Azure SQL-DB, Azure App Service, Azure Kubernetes Service, IAM, CDN,  Azure Functions)
  • GCP (Vertex AI, Kubernetes Engine)

Sprachen

DeutschMutterspracheEnglischverhandlungssicherFranzösischGrundkenntnisse

Projekthistorie

Railyway Capacity Optimization & Train Rerouting, Role: Senior Machine Learning Engineer (Tech Lead)

Transport und Logistik

1000-5000 Mitarbeiter

Development of a Tool, that automatically plans the scheduling of construction sites in combination with rerouting of affected trains.
I developed an algorithm based on (meta)-heuristics that is able to rearrange the train schedule in seconds. A low latency was required for operative use in the planning process.

Tech Stack: Docker, Python, AWS Step Functions/Batch/Fargate/S3, pytorch, Google ORTools, GitLab CI, ArgoCD, Helm, Postgres, FastApi

RAG-ChatBot, Role: Technical Lead/ML Engineer

Internet und Informationstechnologie

>10.000 Mitarbeiter

Development of several RAG-ChatBots based on Azure OpenAI and Pinecone/Postgres. The goal of the project was to develop a template project that can be quickly adapted for additional clients. I tested and benchmarked various RAG approaches to significantly improve the response quality of the ChatBots even with a large number of heterogeneous documents.
TechStack: Azure OpenAI, Azure SQL, Azure Blob Storage, Azure IAM, Azure App Service, Python, Docker, React

Next Best Action (Sales & Marketing), Role: Machine Learning Engineering Specialist (Stream Lead Big Data & Analytics)

Banken und Finanzdienstleistungen

1000-5000 Mitarbeiter

Development of a Next Best Action Engine to support sales managers in the creation of campaigns and bank advisors in the individual support of customers. In addition to the primary goal of the NBA engine by combining existing and new models, processes and tools for the ML lifecycle, testing and deployment and ML monitoring were also introduced to improve the client's data science environment in terms of benefits, quality and stability.
Tech Stack: Docker, Python, Java, Hive/Hadoop/Spark, ML Libraries (tensorflow, pytorch, scikit-learn), Kafka, Bitbucket, Jenkins

Bewertungen

"Felix hat in kürzester Zeit qualitativ hochwertige Arbeit geleistet und alle Erwartungen übertroffen. Er unterstützte uns in technischer Architektur, Beratung, Cloud-Infrastruktur-Setup (+Dokumentation) und Code-Überarbeitung. Sein Fachwissen, seine Programmier-, und Cloud-Kenntnisse hoben unsere Plattform auf ein neues Niveau. Dank seiner Effizienz und der Kommunikation in Muttersprache vermieden wir unnötige Iterationen und minimierten Ausgaben. Ich empfehle seine Dienste wärmstens"

validia ag

Geschäftsführer (Dominique Michel)

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Versichert bis: 01.09.2027


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