Applied AI Engineer (Hybrid)

RTX (Raytheon); Farmington (CT); On-site; USD 140,000 - 190,000; Full time

Job summary

RTX is seeking an experienced Applied AI Engineer to design, build, evaluate, and deploy production-grade AI/ML solutions addressing complex business and engineering problems across RTX. The role blends strong software engineering with hands-on AI/ML expertise, including generative AI, retrieval, and agentic AI.

You will collaborate with AI Architects, Platform Engineers, data teams, product teams, and other engineering groups to move solutions from prototype to secure production environments

Qualifications

• University Degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or related STEM discipline.
• Minimum of 8 years of relevant professional experience.

Responsibilities

• Design, develop, and deploy production-grade AI and ML solutions across RTX.
• Build AI agents and intelligent workflows that reason and interact with enterprise data and tools.
• Develop retrieval and context-engineering solutions using embeddings, vectors, and knowledge sources.
• Evaluate models and select approaches based on quality, reliability, latency, cost, security, and scalability.
• Deliver end-to-end AI software, APIs, integrations, and reusable components.
• Diagnose AI system behavior using telemetry, feedback, and failure analysis.

Education

University Degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or related STEM discipline

Job description

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.

The following position is to join our RTX Enterprise Services team:

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. The ideal candidate combines strong software engineering fundamentals with hands-on AI/ML expertise and experience applying Generative AI, large language models, retrieval, and agentic AI to real-world problems. You will work closely with business teams, AI Architects, AI Platform Engineers, data teams, product teams, and other engineering organizations to take AI solutions from early concepts and prototypes through production deployment and measurable business outcomes. This is a hands-on engineering role for someone who understands how AI systems behave, how they fail, and how to engineer reliable solutions around them.

What You Will Do:

• 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.

What You Will Learn:

• 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 production feedback, evaluation, and operational telemetry can be used to continuously improve AI system quality and business outcomes.

Qualifications You Must Have:

A University Degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or a related STEM discipline and a minimum of 8 years of relevant professional experience, or