AI Integration Architect – AI Accelerator

What matters most

• Productionize research prototypes

  The role converts AI prototypes from RTX R&D teams into production-grade applications, microservices and pipelines deployable on the AI Factory.

• Own reusable service architecture

  Design interfaces and integration patterns for independently developed capabilities, with architecture that supports onboarding, scaling and retirement without significant rework.

• Integrate models with cloud applications

  Build and deploy services on AWS and Azure, integrate LLMs, analytics, perception models and RAG pipelines, and work with the AI Factory infrastructure team as application needs shape the platform.

• Engineer secure, observable delivery

  Implement CI/CD, automated testing, observability and secure deployment workflows while meeting RTX cybersecurity, infrastructure and regulatory requirements.

Likely challenges to think through

• Reuse versus domain-specific needs

  Preparation hypothesis, not a reported problem: capabilities from varied R&D teams may need different interfaces, while the role calls for patterns that avoid per-application rework. Prepare to explain how you would set common interface boundaries while allowing justified exceptions.

• Prototype speed versus production controls

  Preparation hypothesis, not a reported problem: hardening early-stage work for a constrained, regulated environment may require changes that slow deployment. Prepare an example of how you would evaluate production readiness, prioritize gaps, and add testing, observability and security controls.

• Application demands versus shared infrastructure

  Preparation hypothesis, not a reported problem: an application’s deployment needs may not fit a common platform pattern, particularly across AWS and Azure. Prepare to describe how you would distinguish an application-level solution from a reusable platform change and work through that decision with infrastructure partners.

Topics to prepare

• AI capability lifecycle and service architecture

  The posting calls for architecture that lets independently developed capabilities be onboarded, scaled and retired with minimal rework. Revision exercise: sketch service boundaries and a versioned interface for two hypothetical prototypes, then explain how each would be replaced or retired.

• Production hardening in a regulated environment

  RTX describes the AI Factory as a constrained, regulated production environment and requires compliance with its cybersecurity and infrastructure requirements. Revision exercise: make a review checklist for taking a prototype to production, separating security, testing, observability and compliance questions; do not assume specific RTX controls.

• Enterprise AI integration

  The role integrates LLMs, analytics and perception models, and RAG pipelines into applications; LLM and RAG experience is required. Revision exercise: diagram a RAG request path and identify where the application should handle model errors, retrieval failures and changes to the model interface.

• CI/CD and MLOps boundaries

  CI/CD, automated testing and observability are responsibilities; model registries, monitoring and pipelines are preferred MLOps knowledge. Revision exercise: map a proposed release from code and model changes through tests, deployment and monitoring, noting which checks concern the service versus the model.

• Cloud and distributed-service tradeoffs

  The role builds services on AWS and Azure; REST APIs, microservices, SQL and distributed systems are preferred knowledge. Revision exercise: compare two service-boundary designs for an AI application, including failure handling, scaling and data access, without assuming a mandated cloud architecture.

Skills to revise

• Implement a prototype-to-service adapter

  Python, C++ and modern back-end frameworks are required, and the role turns prototypes into deployable services. Practice wrapping a small model stub behind a back-end interface, with validation, error handling and tests; explain where Python and C++ components would meet.

• Build a reusable model integration contract

  Reusable interfaces for AI capabilities from different R&D teams are central to the posting. Practice defining input and output contracts for two hypothetical model types, then write adapter tests that show one implementation can be swapped without changing the calling application.

• Automate a tested release

  The posting requires CI/CD and DevOps automation experience and names GitHub Actions and Jenkins as examples. Practice writing a small pipeline in one of those tools that runs service and integration tests before a deployment step; state what a failed check blocks.

• Exercise an LLM or RAG integration

  LLMs, RAG workflows and application-level AI/ML integration are required. Practice connecting a small application to a model or RAG pipeline, then test empty retrieval results, malformed outputs and model unavailability. This is preparation, not a claim about RTX’s implementation.

• Instrument and secure a cloud deployment

  The role calls for AWS and Azure services, observability and secure deployment workflows. Practice drafting a deployment configuration and operational checks for a small service on either named cloud; identify what should be logged, monitored and reviewed before release, without inventing RTX-specific controls.

• Map evidence to the stated qualifications

  Prepare a one-page evidence map for the posting’s experience thresholds: Python, C++ and back-end frameworks; CI/CD and DevOps; LLMs, RAG and AI frameworks; and AI/ML application integration. Note the project, dates and specific contribution for each, without assuming a match.

• Rehearse a prototype-to-production walkthrough

  Select a relevant project, if available, and explain its starting prototype, architecture decisions, deployment path, measurable results and tradeoffs. The role specifically converts R&D AI prototypes into applications, microservices and pipelines on the AI Factory.

• Sketch a reusable integration design

  As a preparation exercise, draw an interface and service architecture for onboarding, scaling and retiring independently developed AI capabilities with minimal rework. Be ready to justify API boundaries, versioning and how application needs would be discussed with the infrastructure team; the posting does not prescribe a particular design.

• Prepare an AI integration deep dive

  Outline how an LLM, RAG pipeline or other model would be integrated into an enterprise application, including data flow, testing and operational tradeoffs. Distinguish experience you can substantiate from design choices proposed for discussion.

• Document a secure delivery and operations approach

  Prepare a sample delivery diagram or checklist covering CI/CD, automated tests, observability and secure deployment on AWS or Azure. Explain how you would identify applicable cybersecurity and regulatory requirements rather than assuming RTX’s internal controls.

• Gather examples of technical direction and collaboration

  Prepare concise examples, if available, of hardening work for production, resolving an architecture tradeoff and communicating decisions across researchers, infrastructure teams and other stakeholders. These reflect responsibilities and preferred experience in the posting.

• Write targeted questions for the employer

  Ask how AI Factory interfaces and platform responsibilities are divided, what criteria govern production readiness and retirement, and which AWS or Azure deployment constraints apply. Confirm the hybrid onsite expectations for the listed locations if relevant.