Senior Software Engineer AI applications

In this role, you will take technical responsibility for AI application projects within Deutsche Telekom. Working in small, cross-functional teams, you will turn business needs and early concepts into reliable software solutions, covering the full engineering lifecycle from proof of concept and technical validation through production readiness, go-live, and structured handover to MLOps or operations teams. 

Your responsibilities will include: 

• Designing and implementing production-grade backend services, APIs, and integration components for data- and AI-enabled applications  
• Translating business requirements into technical concepts and scalable, secure, maintainable software architectures while driving their implementation within small, cross-functional teams  
• Building production-grade agentic workflows on an existing agentic workflow platform and integrating them with enterprise systems, APIs, data sources, knowledge bases, and business processes  
• Ensuring the reliable and controlled operation of AI applications through automated testing, error handling, fallbacks, guardrails, human-in-the-loop mechanisms, security, observability, traceability, and auditability  
• Developing solutions iteratively from PoC and prototype stages to production, including deployment, go-live support, troubleshooting, and technical stabilization  
• Establishing engineering practices such as code reviews, CI/CD, GitOps, automated integration and end-to-end testing, and technical documentation  
• Ensuring a structured handover to MLOps or operations teams, including deployment procedures, monitoring, runbooks, operational documentation, and knowledge transfer  
• Collaborating with product owners, architects, data scientists, MLOps engineers, security specialists, platform teams, and business stakeholders to evaluate solution options, explain trade-offs, and identify technical risks and dependencies  
• Contributing reusable components, integration patterns, and engineering standards while supporting other engineers through technical guidance, mentoring, and code reviews  
• The role focuses primarily on software engineering for AI-enabled applications rather than pure data science or model research. You will typically use existing models, AI services, and agentic workflow platforms and turn them into reliable, secure, integrated, and operable business applications.

MUST HAVE

• 5+ years of professional software engineering experience, with a strong focus on backend development and Python for production grade systems 
• Solid experience building backend services, APIs, and distributed systems 
• Experience with containers and deployment technologies such as Docker, Kubernetes, or serverless platforms 
• Good understanding of software architecture, clean code principles, and engineering best practices 
• Experience working with Git, pull requests, code reviews, and collaborative development workflows 
• Practical experience with CI/CD and GitOps concepts and tools 
• Ability to work independently, take ownership, and drive topics forward 
• Strong communication skills and the ability to explain technical concepts in clear, simple, and stakeholder-appropriate language 
• An outgoing, collaborative personality and a genuine interest in working with colleagues to find good solutions

The following experience would be a strong plus: 

• Knowledge of data engineering concepts, such as ETL/ELT pipelines, data transformation, batch processing, data quality, or orchestration tools 
• Experience with deployments on hyperscaler platforms such as AWS, Azure, or Google Cloud Platform 
• Experience with AI-enabled backend applications, especially RAG-based systems, semantic search, document Q&A, internal knowledge assistants, or agentic workflows using tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, OpenAI, Azure OpenAI, Anthropic, or similar technologies 
• Familiarity with typical RAG stack components, such as embeddings, chunking, vector databases/search engines like pgvector, Pinecone, Weaviate, Qdrant, OpenSearch, Elasticsearch, or Azure AI Search, as well as observability/evaluation tools such as LangSmith, Langfuse, OpenTelemetry, or Arize Phoenix 
• Experience with monitoring, logging, tracing, and observability tools 
• Full-stack capabilities and experience in building frontends are a big plus 
• Familiarity with security, authentication, authorization, and secure API design 
• Experience mentoring engineers or leading technical initiatives