Your Future, Delivered.

Software Engineer – AI Platform (MCP & AI Integrations)

With a global team of around 5600+ IT professionals, DHL IT Services connects people and keeps the global economy running by continuously innovating and creating sustainable digital solutions. We work beyond global borders and push boundaries across all dimensions of logistics. You can leave your mark shaping the technology backbone of the biggest logistics company of the world. All our locations have earned #GreatPlaceToWork certification, reflecting our commitment to exceptional employee experiences.

Digitalization. Simply delivered.

Join us at DHL Group Digital Platforms department, where innovation thrives and technology evolves. With a presence across three countries and two continents, we are driven to create a Stop Shop for all DHL Group APIs and AI-enabled services. Our startup spirit within a stable, large-scale company framework enables us to integrate Agile and DevSecOps into secure, efficient, and scalable delivery models.

• About the Project:

Be part of an exciting initiative to build the AI Enablement Platform based on the Model Context Protocol (MCP) — enabling secure and seamless communication between enterprise systems and AI models.

This platform extends DHL’s API ecosystem by:

• Leveraging Google Apigee for governance, security, and API lifecycle management
• Running scalable services on Google Kubernetes Engine (GKE) within the GCP platform
• Enabling AI-driven interactions through context delivery, tool access, and orchestration

You will work at the intersection of backend engineering, API design, and AI integration, building scalable systems that power AI-driven applications across DHL.

#DHL #DHLITServices #GreatPlace #digitalplatforms #AI #MCP #APIPlatform

Grow together.

• Your role as Backend Software Engineer – AI Platform:

• Design and implement MCP-compliant services and connectors for secure AI access to external data and tools
• Build backend systems using TypeScript, Node.js, and Express.js to expose structured context and capabilities
• Manage API layers via Apigee, ensuring governance, security, and lifecycle management
• Deploy, monitor, and scale services on Google Kubernetes Engine (GKE)
• Define tool interfaces (actions AI can invoke) and resource schemas / protocol contracts
• Implement observability, logging, and tracing for AI request flows
• Optimize performance, reliability, and latency for AI-driven interactions
• Collaborate with architects and platform teams to contribute to scalable and reusable AI services

• Ready to embark on the journey? Here’s what we are looking for:

• 5+ years of experience in backend development
• Strong proficiency in TypeScript, Node.js, and Express.js
• Experience building API-first architectures and RESTful services
• Hands-on experience with Docker, containerization, and Google Kubernetes Engine (GKE) or similar orchestration platforms
• Practical experience with Apigee, including API proxy development, OAuth2/JWT security, traffic management, and governance
• Strong understanding of distributed systems, microservices architecture, and secure API design principles
• Familiarity with AI/LLM integration concepts such as context injection, tool invocation, and prompt orchestration

Nice to have

• Experience with LLM frameworks such as LangChain or Semantic Kernel
• Exposure to MCP or similar AI integration patterns
• Familiarity with observability tools (e.g., Prometheus, Grafana) and logging/tracing frameworks
• Experience with CI/CD pipelines and DevOps practices
• Knowledge of AI security or governance concepts

• An array of benefits for you:

• Hybrid work arrangements to balance in-office collaboration and home flexibility.
• Annual Leave: 42 days off apart from Public / National Holidays.
• Medical Insurance: Self + Spouse + 2 children. An option to opt for Voluntary Parental Insurance (Parents / Parent -in-laws) at a nominal premium covering pre existing disease.
• In House training programs: professional and technical training certifications.

• Experience with LLM frameworks such as LangChain or Semantic Kernel
• Exposure to MCP or similar AI integration patterns
• Familiarity with observability tools (e.g., Prometheus, Grafana) and logging/tracing frameworks
• Experience with CI/CD pipelines and DevOps practices
• Knowledge of AI security or governance concepts