Applied AI Engineer (Hybrid)

RTX•Cedar Rapids, IA

24d•$107,500 - $204,500•Hybrid

About The Position

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.

Requirements

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 an Advanced Degree in a related field and a minimum of 5 years of relevant professional experience.
A minimum of 3 years of hands-on experience developing, integrating, or deploying AI/ML solutions, including experience taking AI or ML capabilities beyond experimentation into production or production-like environments.
Software engineering experience, including hands-on programming with Python and experience developing production-quality, tested, maintainable software.
Experience building applications using Generative AI and large language models, including prompt or context engineering, model integration, structured outputs, retrieval, or tool use.
Experience integrating software with APIs, databases, enterprise applications, cloud services, or other external systems.
Experience applying software development practices including source control, automated testing, CI/CD, containerization, and production deployment.
Experience working with machine learning fundamentals, model evaluation, and the tradeoffs involved in selecting and applying AI models to business problems.

Nice To Haves

Experience building production AI agents, agentic workflows, or multi-agent systems involving orchestration, tool use, state, memory, and human-in-the-loop interaction.
Experience with retrieval-augmented generation, embeddings, vector databases, enterprise search, knowledge graphs, or advanced context-engineering techniques.
Experience with AI frameworks or platforms such as LangGraph, CrewAI, IBM watsonx, AWS Bedrock, Microsoft AI platforms, n8n, or similar technologies.
Experience with MCP, function or tool calling, secure enterprise integrations, or other agent interoperability patterns.
Experience developing AI evaluation frameworks or using evaluation, tracing, observability, guardrails, and production monitoring to improve AI system quality.
Experience with traditional machine learning, model serving, model lifecycle management, MLOps, or production ML systems.
Experience deploying AI solutions using cloud-native technologies such as Docker, Kubernetes, public cloud services, or hybrid and on-premises environments and familiarity with AI security, Responsible AI, privacy, governance, and the challenges of deploying AI within aerospace, defense, manufacturing, engineering, or other regulated environments.
Demonstrated ability to independently solve complex technical problems, collaborate across multidisciplinary teams, and communicate technical concepts and tradeoffs effectively.

Responsibilities

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.