Applied AI Engineer

RTX

. Design, develop, and deploy production-grade AI and ML solutions using traditional machine learning, Generative AI, retrieval-augmented generation, agentic AI, and software engineering .

Posted 9/17/2026full-timeFarmington • Arizona • United StatesMid-LevelSenior💰 $107,500 - $204,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying AI and ML solutions, with a strong focus on production-quality software, integration with enterprise applications, and model evaluation. Proficient in Python programming and familiar with advanced AI techniques such as Generative AI and retrieval-augmented generation.

Highest-signal resume keywords

AI And ML Solution DevelopmentPython ProgrammingGenerative AI And Large Language ModelsAPI Integration And Enterprise ApplicationsModel Evaluation And Selection

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning FundamentalsProduction-Quality Software DevelopmentContext EngineeringModel IntegrationAutomated TestingCI/CDContainerizationProduction DeploymentAI Evaluation FrameworksMLOps

Tools & Technologies

DockerKubernetesAWS BedrockIBM WatsonxLangGraphN8nVector DatabasesKnowledge GraphsEnterprise SearchModel Context Protocol (MCP)

Industry Keywords

Generative AIAgentic AIRetrieval-Augmented GenerationAI SecurityResponsible AIPrivacyGovernanceRegulated-Environment ExperienceMulti-Agent SystemsAI System Behavior Diagnosis

Tech Stack

Tools & technologies

AWSCloudCyber SecurityDockerKubernetesPython

About the role

Key responsibilities & impact

Design, develop, and deploy production-grade AI and ML solutions using 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 human oversight
Develop retrieval and context-engineering solutions using enterprise data, embeddings, vector and enterprise search, knowledge sources, prompts, and memory
Integrate AI solutions with enterprise applications, APIs, data sources, and tools using standard interfaces and Model Context Protocol (MCP)
Evaluate and select models and solution approaches based on quality, reliability, latency, cost, security, scalability, and business requirements
Develop systematic evaluation cases to measure solution performance
Develop production-quality software, APIs, integrations, tools, and reusable AI components for end-to-end AI solutions
Diagnose and improve AI system behavior using evaluations, traces, telemetry, user feedback, and failure analysis
Address groundedness, task completion, robustness, and production reliability issues
Partner with AI Architecture, Platform Engineering, Data, Evaluation, Cybersecurity, and business teams to move solutions into secure, scalable production environments

Requirements

What you’ll need

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 taking AI or ML capabilities beyond experimentation into production or production-like environments
Hands-on programming with Python
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 with source control, automated testing, CI/CD, containerization, and production deployment
Experience with machine learning fundamentals, model evaluation, and AI model selection tradeoffs
U.S. Person status required: U.S. citizen, U.S. national, lawful permanent resident, or protected individual
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
Preferred: production AI agents, agentic workflows, or multi-agent systems
Preferred: retrieval-augmented generation, embeddings, vector databases, enterprise search, knowledge graphs, or advanced context engineering
Preferred: LangGraph, CrewAI, IBM watsonx, AWS Bedrock, Microsoft AI platforms, n8n, or similar technologies
Preferred: MCP, function/tool calling, secure enterprise integrations, or agent interoperability patterns
Preferred: AI evaluation frameworks, tracing, observability, guardrails, or production monitoring
Preferred: traditional machine learning, model serving, model lifecycle management, MLOps, or production ML systems
Preferred: Docker, Kubernetes, public cloud, hybrid/on-premises environments, AI security, Responsible AI, privacy, governance, or regulated-environment experience

Benefits

Comp & perks

Healthcare benefits
Wellness benefits
Retirement benefits
Work/life benefits
Parental and paternal leave
Flexible work schedules
Achievement awards
Educational assistance
Child/adult backup care
Medical insurance
Dental insurance
Vision insurance
Life insurance
Short-term disability
Long-term disability
401(k) match
Flexible spending accounts
Employee assistance program
Employee Scholar Program
Paid time off
Holidays
Annual short-term and/or long-term incentive compensation programs (eligibility dependent on position and collective-bargaining coverage)