As Hungary's most attractive employer in 2025 (according to Randstad's representative survey), Deutsche Telekom IT Solutions is a subsidiary of the Deutsche Telekom Group. The company provides a wide portfolio of IT and telecommunications services with more than 5300 employees. We have hundreds of large customers, corporations in Germany and in other European countries. DT-ITS recieved the Best in Educational Cooperation award from HIPA in 2019, acknowledged as the the Most Ethical Multinational Company in 2019. The company continuously develops its four sites in Budapest, Debrecen, Pécs and Szeged and is looking for skilled IT professionals to join its team.
Department and Project
This position is within the Tolling Services team of Deutsche Telekom IT Solutions Hungary. The team consists of IT professionals specializing in electronic road-charging solutions. Together with our partners, we develop and operate intelligent, kilometer-based tolling systems for international customers, covering the entire business process (vehicle registrations, country booking, providing On-Board Units, position data transmission, charge calculation, and fleet billing). Leveraging digital intelligence and advanced technology, our tolling solutions facilitate seamless mobility across Europe.
We're looking for a versatile AI Engineer who combines strong software engineering fundamentals with AI/ML expertise. You'll build production-ready, agentic AI solutions that are automated, scalable, and vendor-agnostic. This role emphasizes end-to-end ownership from design through deployment and operations.
Main tasks
As an AI Engineer:
- Software Engineering & Automation
- Write clean, maintainable Python code and automation scripts (bash/shell)
- Build CI/CD pipelines and deployment automation for AI systems
- Create infrastructure as code and configuration management solutions (terraform)
- Develop APIs, microservices, and event-driven architectures
- AI Solution Development
- Design and implement agentic AI systems with autonomous reasoning and tool use
- Build RAG (Retrieval-Augmented Generation) systems and knowledge bases
- Develop multi-agent orchestration and coordination patterns
- Integrate LLMs with business logic and external systems
- Production Deployment & Operations
- Own the complete production deployment lifecycle
- Implement monitoring, logging, alerting, and observability solutions
- Create runbooks, deployment guides, and operational documentation
- Ensure security, scalability, and reliability of AI systems
- Architecture & System Design
- Design vendor-agnostic, portable AI architectures
- Make pragmatic build-vs-buy decisions for components
- Balance managed services with open-source alternatives
- Architect for multi-cloud compatibility where appropriate
- Collaboration & Knowledge Sharing
- Work with operation managers to translate requirements into technical solutions
- Create clear documentation for both technical and non-technical audiences
- Support demos, POCs, and customer presentations
- Share knowledge and best practices with the squad
Candidate Requirements:
Background and required skills:
- 4+ years in software engineering with strong Python and scripting skills
- 2+ years deploying and operating production systems (CI/CD, monitoring, incident response)
- 2+ years working with cloud platforms (AWS preferred, but multi-cloud awareness valued)
- 1-2+ years with LLMs, agentic AI, RAG systems, or production AI/ML systems
- Proven track record: Shipped AI systems to production with full automation and monitoring
Soft Skills & Team Fit
- Ownership mentality: Take responsibility for features from design through production operations
- Pragmatic problem-solver: Balance perfection with delivery, make informed trade-offs
- Clear communicator: Explain technical concepts to both engineers and non-technical stakeholders
- Automation advocate: Default to "automate it" rather than "document the manual steps"
- Self-directed: Drive projects forward with minimal supervision
- Collaborative: Share knowledge, review code, support teammates
- Production-minded: Think about monitoring, security, scalability from the start
- Adaptable: Comfortable with ambiguity, changing requirements, and learning new technologies
- Quality-focused: Write clean code, comprehensive tests, and clear documentation
Advantage:
Advanced Agentic AI
- Experience with agent frameworks (LangGraph, CrewAI, AutoGen, Strands)
- Agent memory systems (short-term, long-term, semantic memory)
- Agent evaluation and benchmarking methodologies
- Human-in-the-loop patterns for agent systems
- Agent safety, guardrails, and responsible AI practices
Domain Knowledge
- Experience in telecommunications or enterprise IT
- Log analysis and operational intelligence systems
- Document processing and information extraction
- Customer-facing AI solution delivery