Senior MLOps Engineer (d/f/m)

GENERAL INFORMATION

Location: Bonn, Germany

Job Seniority: Professionals

Job Type: Non Consulting

Working time: Full-time

Type of contract: Permanent

Travel extent: None

Description and Requirements

Creating value at DHL Group through data and AI innovation!

At DHL Data & AI, our primary objective is to design and deliver scalable AI solutions that create tangible business value across DHL Group. We work end-to-end, from small, fast Proof of Concepts that validate potential to fully deployed AI solutions built in close partnership with business and engineering teams. To support this, we continuously optimize our data and AI platforms to ensure secure, cost-efficient, and reliable foundations for our solutions.

Alongside this core focus, we also strengthen the organization through data governance, training, technology incubation, and community building. We are committed to scaling Data & AI capabilities sustainably and accelerating digitalization across DHL Group.

Join our growing team in Bonn full time as Senior MLOps Engineer (d/f/m) and shape the company further!

Who we are:

DHL Data & AI is a Global Business Service Line in DHL Group, focusing on leveraging data to create business value. We are a dynamic, innovative, and diverse team with approximately 100 talented professionals, including Data Engineers, Cloud Engineers, DevOps Engineers, Data Scientists, and Architects.

DHL Group is the leading mail and logistics service provider in the world. As one of the planet's largest employers operating in over 220 countries and territories, we see the world differently.

Your tasks:

DHL Data & AI operates a diverse portfolio of production-grade AI solutions across the Group, spanning LLM-based and agentic applications as well as traditional machine learning models. Built on a modern Kubernetes platform, our ecosystem blends leading open-source technologies with carefully chosen commercial solutions, providing an innovative and robust foundation for engineering excellence.

Our Data Scientists and AI Engineers create the models and business logic that drive innovation. MLOps Engineers bridge the gap between development and production, turning these concepts into robust, secure, and observable services. As a Senior MLOps Engineer, you take ownership of this journey from start to finish. You design the architecture, implement the solution yourself, and apply your expertise and judgment to determine when it is ready for real-world use.

What you will do:

Own the architecture of production-grade AI solutions.

You design and shape the end‑to‑end architecture of agentic and GenAI applications, defining how they interact with business systems, internal model gateways, data sources, and users. From orchestrating multi‑step agent workflows to designing data flows, state management, persistence, and failure handling, you make the critical architectural decisions that enable scalable and reliable solutions. You clearly document and communicate those decisions, ensuring the team understands not only what was built, but why.

Build solutions hands‑on.

This is a highly technical, hands‑on engineering role. You develop applications yourself using Python and modern frameworks such as FastAPI and Pydantic, leveraging agent frameworks and cloud‑native technologies. You containerize and package solutions, automate deployments through CI/CD and GitOps practices, and deliver them across development, UAT, and production environments on multiple cloud platforms.

Drive production readiness and Go‑Live decisions.

You take ownership of a solution's readiness for production and help ensure a smooth transition to operations. You evaluate security, identity and access management, resilience patterns, observability, backup and recovery strategies, operational runbooks, and support processes. Your judgment helps determine when a solution is ready to reliably serve business users at scale.

Make AI systems measurable and continuously improve them.

You establish visibility into the behavior and performance of LLM‑powered applications through distributed tracing, observability platforms, and evaluation frameworks. You develop evaluation datasets and automated quality assessments, including LLM‑as‑a‑judge approaches, helping teams maintain high quality while balancing cost, performance, and latency over time.

Operationalize machine learning at scale.

You design and manage pipelines for data preparation, model training, batch inference, and recurring ML workloads. Working with technologies such as Kubeflow and Databricks, you help Data Scientists transform exploratory work into structured, reusable, and production‑ready pipelines and model services.

Act as a technical leader and mentor.

You raise engineering standards through thoughtful code reviews, architectural guidance, and knowledge sharing. You support the growth of Associate and Mid‑Level Engineers and help build a culture where expertise is documented, shared, and embedded within the team rather than relying on individual contributors.

What you bring:

• A Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, Physics, (Industrial) Engineering, or a related technical field.
• 4+ years of professional experience designing, building, and operating production‑grade software, data, or machine learning systems.
• Hands‑on experience developing agentic or Generative AI solutions, including multi‑step agent workflows, tool and function calling, integration with LLM providers, and connectivity to business applications. You understand the practical challenges and failure modes of operating these systems in production.
• Strong Python skills, with experience writing structured, typed, and well‑tested code. You are comfortable working within application codebases, not just infrastructure or automation scripts (e.g., FastAPI, Pydantic).
• Solid experience with containers and Kubernetes, Helm‑based deployments, GitOps practices, and CI/CD automation on at least one major cloud platform.
• Knowledge of cloud infrastructure, databases, data engineering, or related technology domains.
• Experience with machine learning and LLM lifecycle platforms and tooling, such as Kubeflow, MLflow, AWS SageMaker, Azure Machine Learning, or Google Vertex AI.
• Familiarity with secrets management, service mesh technologies, and identity and access management in Kubernetes environments (e.g., Vault, Istio, OAuth2 Proxy, OIDC).
• Proven experience taking systems from development to production. You have personally owned production readiness, security, resilience, observability, and operational handover for at least one business‑critical system with real users.
• Fluency in English, German language skills are a plus.

What will help you succeed in this role:

• The 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, with a pragmatic understanding of when to apply more advanced techniques.
• A broad perspective across both generative AI and traditional machine learning, including experience working with production‑grade systems at scale.
• A passion for mentoring others, fostering engineering excellence, and helping teams grow through collaboration and constructive feedback.

Additional experience that would stand out:

• Working with LLM observability, tracing, and evaluation platforms to measure, monitor, and improve the quality of AI systems.
• Designing retrieval‑augmented generation (RAG) solutions and 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, or bringing experience with additional programming languages and ecosystems.

What we offer:

At DHL Data & AI, we offer an array of benefits designed to support your professional growth and personal well‑being. Here’s what you can expect when you join our team:

• Comprehensive training programs, workshops, and online learning resources to prioritize your growth and development.
• A flexible work arrangement that blends remote work and office presence, promoting work‑life balance and adaptability.
• A competitive salary package, inclusive of various benefits, to ensure employee well‑being and satisfaction.
• Diverse commuting options, including a job ticket for public transportation, parking space for a monthly fee, or bicycle leasing, to suit your preference.
• Access to DHL Group’s daycare facilities for working parents.
• A vibrant, cooperative, and international environment that inspires innovative thinking.

Requirements:

We are committed to equal opportunity and reject any forms of discrimination as defined in DHL Group’s Human Rights Policy Statement. The basis for employee selection at DHL Data & AI is qualification, performance, skills and experience.

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