FlexIT Consulting GmbH

Business Intelligence Developer

• Critically review existing BI processes and solutions, identify weaknesses and improvement opportunities, and develop solution concepts
• Bring order to heterogeneous data sources: consolidate and cleanly prepare data using Microsoft SQL Server BI tools
• Actively participate in building and expanding the data warehouse and analytical models: migrate relational data into the DWH and continuously evolve underlying data structures and models
• Investigate functional discrepancies in BI models; reconcile and ensure a consistent and reliable data foundation

Data & BI Engineer

Solutions Engineer

• End-to-End-Verantwortung für KI-Skills & Agenten: von der Use-Case-Idee bis zur API-basierten Bereitstellung im laufenden Betrieb
• Prozesse mit LLMs & Tools (u. a. Langdock) in belastbare Lösungen überführen – inkl. Modellwahl zwischen High-End- und kosteneffizienten Modellen
• Schnittstellen & Systemanbindungen eigenständig konzipieren und umsetzen – u. a. Anbindung von SAP als führendem System
• Produktivbetrieb sauber vorbereiten: Testing, Dokumentation, Versionierung, Rollout-Begleitung direkt in den Fachbereichen
• Mitgestaltung des Governance-Modells sowie Wissenstransfer für den langfristigen internen Weiterbetrieb
• Kritische, fundierte Bewertung von Modellen, Plattformen und technischen Stacks – technisch, wirtschaftlich und ethisch
• Aufbau und Betrieb von Cloud- und On-Premise-Infrastruktur (Microsoft Azure inkl. Azure AI Foundry, Linux)

AI Integration Engineer – LLM Agents, RAG

LLM Engineer

• Development of AI skills and agents – from concept to production deployment
• Building RAG pipelines and connecting them to enterprise data
• Selecting and integrating suitable LLMs depending on the use case
• Collaborating with business units to automate processes

Agentic AI Engineer, RAG, LLM

QA Automation Engineer

• Design, build, and maintain infrastructure automation solutions across hybrid environments
• Develop and manage Infrastructure as Code (IaC) using tools such as Terraform, Ansible, or PowerShell
• Integrate automation into CI/CD pipelines to improve efficiency, reliability, and scalability
• Collaborate with infrastructure, DevOps, platform, application, and security teams
• Contribute to automation standards, documentation, and governance

Infrastructure Automation Engineer, m, f, x

Product Manager

• Establish AI in engineering
• Further develop AI across the entire software stack with a strong focus on innovation, practical implementation, scalability and sustainable integration.
• Orchestrate agent-based systems
• Design, build and manage AI agents and their interactions within complex system landscapes
• Design use cases and architectures
• Identify meaningful areas of application and develop scalable architectural approaches
• Transition prototypes into production
• Turn ideas and PoCs into productive, stable solutions
• Optimize DevOps & workflows
• Use AI to improve development processes, CI/CD, GitOps and operational procedures
• Hands-on technical work
• Develop and integrate modern AI solutions (LLMs, RAG systems, agent architectures)
• Further develop own framework
• Work with and expand an existing internal framework for AI-supported development and automation
• Ensure quality and compliance
• Consider regulatory requirements, traceability and security, testing

AI Product Owner

• Establish AI within engineering
• Advance AI across the entire software lifecycle with a strong focus on innovation, practical implementation, scalability, and sustainable integration.
• Orchestrate agent-based systems
• Design, build, and manage AI agents and their collaboration within complex system landscapes
• Define use cases and architectures
• Identify meaningful application areas and develop scalable architectural approaches
• Bring prototypes into production
• Transition ideas and PoCs into productive, stable solutions
• Optimize DevOps & workflows
• Use AI to improve development processes, CI/CD, GitOps, and operational procedures
• Technical design and implementation
• Develop and integrate modern AI solutions (LLMs, RAG systems, agent-based architectures)
• Evolve an internal framework
• Work with and expand an existing internal framework for AI-driven development and automation
• Ensure quality and compliance
• Consider regulatory requirements, traceability and security, and testing

Data & BI Engineer

AI Integration Engineer – LLM Agents, RAG

Agentic AI Engineer, RAG, LLM

Infrastructure Automation Engineer, m, f, x

AI Product Owner

AI Product Owner – m,w,d