Software Engineer – AI Platform (MCP & AI Integrations)

What matters most

• Build MCP services and connectors

  The role designs MCP-compliant services that give AI applications secure access to external data and tools. Prepare to explain how you would define tool interfaces, resource schemas and protocol contracts.

• Deliver the TypeScript backend

  The posting calls for TypeScript, Node.js and Express.js services that expose structured context and capabilities. Focus preparation on API-first design and maintainable backend implementation.

• Govern and secure the API layer

  Managing APIs through Apigee is a stated duty, with proxy development, OAuth2/JWT security, traffic management and lifecycle governance named in the requirements.

• Operate services on GKE

  The role deploys, monitors and scales services on GKE. Be ready to discuss containerized deployment and the reliability considerations of distributed services.

• Make AI request flows observable and reusable

  The posting calls for logging and tracing AI requests, improving performance and latency, and working with architects and platform teams on scalable, reusable services.

Likely challenges to think through

• Useful tool access versus controlled access

  Preparation hypothesis, not a reported problem: exposing enterprise data and actions to AI could require balancing useful context against secure API boundaries. Prepare a design example showing how contracts, authorization and validation would limit what a tool can access or invoke.

• Gateway governance versus responsiveness

  Preparation hypothesis: Apigee security and traffic policies may add complexity to latency-sensitive AI interactions. Prepare to explain where you would place controls, how you would measure their effect and how you would evolve API contracts safely.

• End-to-end diagnosis versus observability overhead

  Preparation hypothesis: requests spanning connectors, APIs and GKE services may be hard to diagnose without adding excessive logging or tracing cost. Prepare an approach to correlating requests, choosing useful signals and using them to investigate reliability or latency.

Topics to prepare

• MCP contracts and secure access

  The role designs MCP-compliant services, connectors, tool interfaces and resource schemas. As preparation, sketch a contract for a read-only data resource and an invokable action; explain how each limits what an AI application can access.

• API-first and distributed-system design

  The posting requires RESTful services and understanding of microservices and distributed systems. As preparation, diagram an API-backed context service and explain its boundaries, failure handling and versioning choices.

• API security and governance

  Apigee proxy development, OAuth2/JWT security, traffic management and governance are explicit requirements. As preparation, trace an authenticated request through a proxy and explain where authorization, traffic controls and lifecycle decisions belong.

• LLM integration flow

  The posting calls for familiarity with context injection, tool invocation and prompt orchestration. As preparation, trace a hypothetical AI request from context retrieval to tool response, identifying where untrusted data must not become an instruction.

• Container orchestration and service reliability

  Docker and GKE or similar orchestration experience are required; deploying and scaling on GKE is part of the role. As preparation, explain how a containerized service would be deployed, scaled and kept available during a failure.

• Observability and latency

  The role implements logging and tracing for AI request flows and optimizes performance and reliability. As preparation, map the stages of a request and identify what to log or trace to locate a slow connector without exposing sensitive context.

Skills to revise

• Build a TypeScript context service

  Practice implementing a small Node.js and Express.js REST service that returns validated, structured context. This exercises the posting’s required backend stack and API-first work; the sample service is a preparation exercise, not a stated DHL project.

• Implement a tool connector

  Practice defining a typed input and output contract for a mock action, then implement its connector with validation and bounded error responses. This reflects the role’s MCP-compliant connector and tool-interface responsibilities.

• Configure an Apigee proxy

  In a practice environment, configure a proxy for the sample service with OAuth2/JWT validation and a traffic-control policy; test accepted and rejected requests. This targets the explicitly required Apigee security and traffic-management experience.

• Containerize and deploy

  Package the sample service with Docker and, where a practice environment is available, deploy and scale it on GKE or a similar orchestration platform. This mirrors the required containerization experience and GKE deployment responsibility.

• Instrument an AI request path

  Add request logging and tracing across a sample context lookup and tool invocation, then use the records to locate a simulated failure or delay. This practices the posting’s observability, reliability and latency work.

• Prepare a backend project walkthrough

  The posting calls for 5+ years of backend development and strong TypeScript, Node.js and Express.js proficiency. Choose a relevant project you can discuss and outline your API design, implementation decisions, trade-offs and measurable results.

• Sketch an MCP connector design

  The role involves MCP-compliant services that expose structured context and tools. As a preparation exercise, draw a connector to an enterprise API and define a sample tool interface, resource schema and protocol contract; mark the design as hypothetical.

• Work through an Apigee security scenario

  The posting specifies Apigee proxies, OAuth2/JWT, traffic management and governance. Prepare an example showing where authentication, authorization, traffic controls and API lifecycle decisions would sit, using actual experience where available.

• Explain a container deployment and failure case

  The role deploys services on GKE, and the requirements allow GKE or similar orchestration experience. Prepare a Docker-to-orchestrator walkthrough covering scaling, service failures and secure API design; distinguish hands-on work from proposed choices.

• Map an AI request from tool call to response

  The posting names context injection, tool invocation and prompt orchestration. Trace a hypothetical request through context retrieval and a tool call, explaining what data is exposed and where security checks belong. Treat MCP and LLM frameworks as preferred exposure, not required experience.

• Prepare a reliability and observability example

  The role calls for logging, tracing and improvements to latency and reliability. Bring a concrete example, if available, with the signals you used, the bottleneck you found and the outcome; otherwise sketch what you would measure across an AI request flow.

• Draft questions for the platform team

  Ask how architects and platform teams divide ownership of reusable connectors, how Apigee and GKE fit the AI Enablement Platform, and what security, latency and observability challenges the team is prioritizing.