AI Platform Engineer (Hybrid)

What matters most

• Reusable production services

  The role builds and operates scalable APIs and distributed services for AI applications, models, and agents. Prepare to explain backend design decisions, tested production code, and how input from application and AI teams becomes a reusable platform capability.

• AI runtime integration

  The posting calls for model access and routing, AI gateways, agent runtimes, tool integration, model serving, and retrieval services. Prepare to discuss how these components fit together and where their service boundaries belong.

• Identity and controlled access

  Responsibilities include agent and application identity, authentication, authorization, secrets, permissions, and enterprise-system integration. Prepare to explain how you would enforce access at service and tool boundaries.

• Automated deployment across environments

  The role automates development, test, and production deployments with containers, Kubernetes, CI/CD, and infrastructure as code, while supporting cloud, hybrid, on-premises, and restricted environments. Prepare to describe deployment and release decisions that preserve reliability and security.

• Observable AI operations

  The posting calls for logging, metrics, tracing, alerting, execution telemetry, cost visibility, AI evaluation, model lifecycle management, and versioning. Prepare to connect these capabilities to troubleshooting and operating a production platform.

Likely challenges to think through

• Reuse versus application-specific needs

  A likely design tension is providing common services without forcing every AI application into the same workflow. Prepare an example of how you would gather needs across teams, choose stable platform interfaces, and leave room for application-specific behavior; this is an inference, not a reported RTX problem.

• Consistency across computing environments

  Commercial cloud, hybrid, on-premises, and restricted deployments may offer different services and operating constraints. Prepare to explain what you would standardize across environments and what you would adapt, including the reliability and maintenance trade-offs; the posting does not specify particular constraints.

• Agent utility versus access control

  Tool-enabled agents need useful integrations, but broad permissions can increase security and operational risk. Prepare to reason through scoped identity, authorized tool execution, secrets handling, and auditability without assuming a particular RTX agent architecture.

Topics to prepare

• Backend platform architecture

  The posting requires experience designing APIs, microservices or distributed services and emphasizes reusable capabilities. Suggested exercise: sketch an API and service boundary for a shared model-access service; explain scaling, failure handling and how multiple applications would use it.

• Cloud-native delivery

  Docker, Kubernetes, CI/CD, infrastructure as code and deployment on a major public cloud are required experience. Suggested exercise: diagram promotion from development to test to production, including configuration changes and rollback.

• Enterprise identity and security

  The role requires authentication, authorization, identity and access management, secrets management, network security and secure integration. Suggested exercise: map how an application or agent obtains identity and receives narrowly scoped permission to call a tool.

• Production reliability and observability

  Logging, metrics, tracing, monitoring, alerting, troubleshooting and reliability are required; the duties also mention execution telemetry and cost visibility. Suggested exercise: define the signals that would distinguish a slow model call from an unavailable routing service and explain an alert response.

• AI platform lifecycle

  The duties include model routing, serving, retrieval, evaluation, versioning and production operations. Agent runtimes and Model Context Protocol are preferred experience. Suggested exercise: trace a request through a model gateway, optional retrieval and model response, noting where versions, evaluation results and permissions belong.

Skills to revise

• Build a tested backend API

  The posting requires maintainable production software in Python, Java, C# or another modern backend language. Suggested exercise: implement a small model-access API with input validation, error handling and tests; explain how its contract could be reused.

• Package and deploy a service

  The posting requires Docker, Kubernetes, CI/CD, infrastructure as code and public-cloud deployment experience. Suggested exercise: containerize the API, define its Kubernetes deployment and outline a CI/CD pipeline and infrastructure-as-code changes. Use a public cloud only if available; no provider is specified.

• Enforce tool-access permissions

  The duties call for secure agent and application identity, authorization, secrets management and controlled tool access. Suggested exercise: add an authorization check to a tool-invocation endpoint, deny an unauthorized request and describe how credentials stay out of source code.

• Instrument and troubleshoot a failure

  Production observability and operations are required. Suggested exercise: add request logs, latency and error metrics, and tracing to the API; use a simulated downstream failure to walk through diagnosis and an appropriate alert.

• Prototype model routing

  Model access and routing are listed duties, while AI gateways and model-serving experience are preferred. Suggested exercise: route requests between two stub model endpoints using a documented selection rule, then test fallback behavior and record which route handled each request.

• Build a requirements evidence sheet

  The posting specifies degree and experience thresholds, backend programming, cloud-native deployment, operations, enterprise security, U.S. person status, and commuting distance to a listed city. Prepare a one-page sheet with verifiable evidence for each item; leave any unsupported item blank.

• Rehearse a production platform walkthrough

  Choose a relevant project, if available, and prepare a concise account of its APIs or distributed services, your hands-on contributions, testing, design tradeoffs, production outcomes, and collaboration across teams. The role emphasizes building reusable capabilities, not just deploying infrastructure.

• Sketch an enterprise AI platform design

  As a practice exercise, draw an API-to-model request flow covering model access or routing, an AI gateway, agent tool integration, and retrieval. Explain how the components remain reusable and what changes across cloud, on-premises, or restricted environments; these are responsibilities named in the posting.

• Prepare a security design review

  Create a short threat-and-control table for agent identity, authentication, authorization, secrets, enterprise-system integration, and controlled tool execution. Be ready to explain permission boundaries and failure handling; the posting explicitly calls for secure agent and application capabilities.

• Show how you deploy and operate services

  Prepare a deployment diagram or example using containers, Kubernetes, CI/CD, infrastructure as code, and development, test, and production environments. Add the logging, metrics, traces, alerts, troubleshooting, and reliability decisions you would explain in an interview.

• Outline AI lifecycle and cost visibility

  Draft a brief example of how a platform could version configuration and models, support evaluation and production monitoring, and expose execution telemetry and cost. Treat MCP, vector databases, Responsible AI, and FinOps as preferred areas to discuss only where you have relevant experience.

• Prepare cross-team decision examples

  Identify examples, if available, of translating AI, application, cybersecurity, data, or product needs into shared platform capabilities. For each, note the competing requirements, decision you influenced, and how you checked whether the result helped developers.

• Write targeted questions for RTX

  Ask which reusable capability is the immediate priority, how teams divide responsibility for security and production operations, which deployment environments matter first, and how the hybrid onsite schedule is set with the leader. These questions follow from the advertised role; they do not assume a particular team process.