Date Posted:
2026-09-17
Country:
United States of America
Location:
US-CT-FARMINGTON-0004 ~ 4 Farm Springs Rd ~ 4 FARM SPRINGS
Position Role Type:
Hybrid
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

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 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.

Qualifications We Prefer

• 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.

What We Offer

Whether you're just starting out on your career journey or are an experienced professional, we offer a robust total rewards package with compensation; healthcare, wellness, retirement and work/life benefits; career development and recognition programs. Some of the benefits we offer include parental (including paternal) leave, flexible work schedules, achievement awards, educational assistance and child/adult backup care.

Learn More & Apply Now!

Work Location: This is a hybrid role, eligible candidates must reside within commuting distance of Farmington, CT, El Segundo, CA, San Jose, CA, Tucson, AZ, McKinney, TX, Andover, MA, Cedar Rapids, IA, or Charlotte, NC.

Please ensure the role type defined below is appropriate for your needs before applying to this role. This position is classified as:

Hybrid: Employees who are working in Hybrid roles will work regularly both onsite and offsite. Ratio of time working onsite will be determined in partnership with your leader.

As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote.

The salary range for this role is 107,500 USD - 204,500 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills.

Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement.

Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance.

This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply.

RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window.