Applied AI Engineer; Hybrid

At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world’s most complex problems. With our three market leading businesses, world‑class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world.

Join us and help shape the future of aerospace and defense.

We are seeking an experienced Applied AI Engineer to design, build, evaluate, and deploy production‑grade Artificial Intelligence and Machine Learning solutions that address complex business and engineering problems across RTX.

U.S. Citizen, U.S. Person, or Immigration Status Requirements:
This job requires a U.S. Person. A U.S. Person is a lawful permanent resident as defined in 8 U.S.C. 1101(a)(20) or who is a protected individual as defined by 8 U.S.C. 1324b(a)(3). U.S. citizens, U.S. nationals, U.S. permanent residents, or individuals granted refugee or asylee status in the U.S. are considered U.S. persons. For a complete definition of “U.S. Person” go here.

https://www.ecfr.gov/current/title-22/chapter-I/subchapter-M/part-120/subpart-C/section-120.62

Security Clearance Type:
None/Not Required Security Clearance Status:
Not Required

• Design, develop, and deploy production‑grade AI and ML solutions using the appropriate combination of traditional machine learning, Generative AI, retrieval‑augmented generation, agentic AI, and software engineering.
• Build AI agents and intelligent workflows that reason, use tools, interact with enterprise applications and data, and execute complex multi‑step processes with appropriate human oversight.
• Develop retrieval and context‑engineering solutions using enterprise data, embeddings, vector and enterprise search, knowledge sources, prompts, memory, and other grounding techniques.
• Integrate AI solutions with enterprise applications, APIs, data sources, and tools using standard interfaces and emerging interoperability approaches such as Model Context Protocol (MCP).
• Evaluate and select models and solution approaches based on quality, reliability, latency, cost, security, scalability, and business requirements, and develop systematic evaluation cases to measure solution performance.
• Develop production‑quality software, APIs, integrations, tools, and reusable AI components required to deliver end‑to‑end AI solutions while leveraging enterprise platform capabilities wherever appropriate.
• Diagnose and improve AI system behavior using evaluations, traces, telemetry, user feedback, and failure analysis, and address issues related to groundedness, task completion, robustness, and production reliability.
• Partner with AI Architecture, Platform Engineering, Data, Evaluation, Cybersecurity, and business teams to move solutions from experimentation into secure, scalable production environments.

• How AI and ML technologies are applied to complex business, engineering, manufacturing, and operational challenges across a global aerospace and defense enterprise.
• How Generative AI and agentic AI systems are engineered to securely interact with enterprise data, applications, APIs, tools, and workflows.
• How enterprise AI platforms provide reusable capabilities for models, agents, tools, identity, deployment, evaluation, and observability across multiple RTX business units.
• How to design and evaluate AI systems across commercial cloud, hybrid, on‑premises, and restricted computing environments.
• How emerging models, agent frameworks, interoperability standards, and AI engineering practices can be evaluated and applied to practical enterprise problems.
• How…