AI Integration Architect – AI Accelerator

RTX•East Hartford, CT

77d•$157,200 - $298,800•Hybrid

About The Position

The AI Accelerator works across RTX to mature early-stage AI prototypes and deploy them as secure, scalable applications on the AI Factory, RTX’s common AI infrastructure. This role is responsible for building enterprise-ready AI solutions based on models and capabilities developed by research and development teams across RTX.

Requirements

Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and a minimum of 12 years of prior relevant experience unless prohibited by local laws/regulations.
Minimum 5 year in Python, C++, and modern back-end frameworks.
Minimum 3 year experience with CI/CD pipelines and DevOps automation tools (GitHub Actions, Jenkins, etc.).
Minimum 1 year with LLMs, RAG workflows, and AI frameworks (PyTorch, TensorFlow).
Minimum 1 year with integrating AI/ML models into software applications.

Nice To Haves

Demonstrated experience shipping AI or software systems into regulated or compliance-constrained production environments.
Experience evaluating and hardening in-flight technical work against production and compliance criteria.
Knowledge of MLOps practices (model registry, monitoring, pipelines).
Experience deploying applications on AWS and/or Azure.
Knowledge of REST APIs, microservices, SQL databases, and distributed systems.
Experience with Docker and Kubernetes.
Experience delivering solutions in regulated or security-conscious environments.
Strong problem-solving skills, initiative, and ability to define technical direction.
Strong analytical, problem-solving, written/verbal communication, and interpersonal skills with track record of teamwork, adaptability, innovation, and initiative.
Clear and effective communication with all levels of management, business development, researchers, and customers.
Ability to approach open-ended tough problems as an opportunity to innovate.
Ability to focus on results in a fast-paced, dynamic team environment.
Ability to work independently with limited direction and in multidisciplinary environment.

Responsibilities

Convert AI prototypes into production-grade applications, microservices, and pipelines deployable on the AI Factory, a constrained, regulated production environment.
Design interfaces and integration patterns that allow independently developed AI capabilities, from varied domains and R&D teams, to plug into the AI Factory, avoiding significant rework per application.
Own service architecture decisions that enable new AI capabilities to be onboarded, scaled, and retired with minimal rework.
Partner with the AI Factory infrastructure team to influence platform and infrastructure evolution based on real application demands.
Build and deploy cloud-based services on AWS and Azure following RTX best practices.
Integrate AI/ML models (LLMs, analytics, perception, RAG pipelines) into enterprise applications.
Implement CI/CD, automated testing, observability, and secure deployment workflows.
Ensure compliance with RTX cybersecurity, infrastructure, and regulatory requirements.
Contribute reusable components and patterns that strengthen the AI Factory platform.