DHL Group
Team Lead MLOps (m/w/x)
Vollzeitmit HomeofficeSenior
Bonn
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Designing and operating agentic and GenAI applications for DHL Group, integrating AI with business systems and orchestrating multi-step workflows. Hands-on experience with agentic/GenAI solutions and Python required. Remote work, flexible arrangement.
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Anforderungen
- Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, Physics, (Industrial) Engineering, or related technical field
- 4+ years professional experience designing, building, operating production-grade software, data, or ML systems
- Hands-on experience developing agentic or Generative AI solutions, including multi-step agent workflows, tool/function calling, LLM provider integration, and business application connectivity
- Understanding of practical challenges and failure modes of operating AI systems in production
- Strong Python skills, with experience writing structured, typed, well-tested code
- Comfortable working within application codebases (e.g., FastAPI, Pydantic)
- Solid experience with containers and Kubernetes, Helm-based deployments, GitOps practices, and CI/CD automation on major cloud platform
- Knowledge of cloud infrastructure, databases, data engineering, or related technology domains
- Experience with machine learning and LLM lifecycle platforms and tooling (e.g., Kubeflow, MLflow, AWS SageMaker, Azure ML, Google Vertex AI)
- Familiarity with secrets management, service mesh technologies, and IAM in Kubernetes (e.g., Vault, Istio, OAuth2 Proxy, OIDC)
- Proven experience taking systems from development to production
- Personal ownership of production readiness, security, resilience, observability, and operational handover for business-critical systems
- Fluency in English
- Ability to anticipate risks and continuously improve systems before issues impact users or business outcomes
- Strong expertise in LLM technologies, model serving, and modern AI architectures
- Pragmatic understanding of when to apply advanced AI techniques
- Broad perspective across generative AI and traditional machine learning
- Experience working with production-grade systems at scale
- Passion for mentoring others, fostering engineering excellence, and helping teams grow through collaboration and constructive feedback
- Working with LLM observability, tracing, and evaluation platforms
- Designing retrieval-augmented generation (RAG) solutions
- Implementing vector-based search and retrieval architectures
- Building and operating durable workflows and data pipelines using modern orchestration platforms
- Contributing to open-source projects
- Exploring new technologies
- Experience with additional programming languages and ecosystems
Aufgaben
- Own the end-to-end architecture of agentic and GenAI applications
- Define how AI solutions interact with business systems, internal model gateways, data sources, and users
- Orchestrate multi-step agent workflows
- Design data flows, state management, persistence, and failure handling
- Document and communicate architectural decisions
- Develop applications using Python, FastAPI, and Pydantic
- Leverage agent frameworks and cloud-native technologies
- Containerize and package solutions
- Automate deployments through CI/CD and GitOps practices
- Deliver solutions across development, UAT, and production environments on multiple cloud platforms
- Evaluate security, identity and access management, and resilience patterns
- Evaluate observability, backup and recovery strategies
- Evaluate operational runbooks and support processes
- Establish visibility into LLM-powered application behavior and performance
- Implement distributed tracing, observability platforms, and evaluation frameworks
- Develop evaluation datasets and automated quality assessments
- Utilize LLM-as-a-judge approaches for quality assessment
- Design and manage pipelines for data preparation, model training, and batch inference
- Manage recurring ML workloads
- Help Data Scientists transform exploratory work into production-ready pipelines and model services
- Raise engineering standards through code reviews and architectural guidance
- Share knowledge with the team
- Support the growth of Associate and Mid-Level Engineers
- Help build a culture of documented and shared expertise
Berufserfahrung
4 Jahre
Ausbildung
Master-Abschluss
Sprachen
Englisch – fließend
Deutsch – Grundkenntnisse
Tools & Technologien
FastAPIPydanticKubernetesHelmGitOpsCI/CDAWSAzureGoogle CloudKubeflowMLflowAWS SageMakerAzure Machine LearningGoogle Vertex AIVaultIstioOAuth2 ProxyOIDC
Benefits
Weiterbildungsangebote
- Comprehensive training programs
- Workshops
- Online learning resources
Flexibles Arbeiten
- Flexible work arrangement
- Remote work
Sonstige Vorteile
- Office presence
Öffi Tickets
- Job ticket for public transportation
Parkplatz & Pendelvorteile
- Parking space for a monthly fee
Firmenfahrrad
- Bicycle leasing
Kinderbetreuung
- DHL Group daycare facilities
Lockere Unternehmenskultur
- Cooperative and international environment
Von Nejo automatisch aufbereitet
Nejo hat diesen Job automatisch von der Website des Unternehmens DHL Group erfasst und die Informationen auf Nejo mit Hilfe von KI für dich aufbereitet. Trotz sorgfältiger Analyse können einzelne Informationen unvollständig oder ungenau sein. Bitte prüfe immer alle Angaben in der Originalanzeige! Inhalte und Urheberrechte der Originalanzeige liegen beim ausschreibenden Unternehmen.
Zur Originalanzeige bei DHL GroupÜber das Unternehmen
DH
LogisticsBonn
DHL Group is the biggest logistics company in the world, providing management consulting and digitalization services across all industries.
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