Hybrid Applied AI Engineer: Build Production AI

RTX; San Jose (CA); Hybrid; USD 140,000 - 210,000; Full time

Job summary

RTX is seeking an experienced Applied AI Engineer to design, build, evaluate, and deploy production‑grade AI/ML solutions across RTX enterprise systems. You will collaborate with AI architects, platform engineers, data teams, and product groups to move AI from concept to production with measurable business impact.

The role emphasizes hands‑on engineering, understanding AI behavior, failure modes, and building robust, secure AI solutions within a hybrid work environment.

Qualifications

• Minimum 8 years of relevant professional experience with AI/ML in production or production‑like environments.
• 3+ years hands‑on experience developing or deploying AI/ML solutions beyond experimentation.
• Proficient in Python and delivering production‑quality software.

Responsibilities

• Design, develop, and deploy production‑grade AI and ML solutions across RTX.
• Build AI agents and workflows that reason, use tools, and interact with enterprise data.
• Develop retrieval and context‑engineering solutions using embeddings and enterprise search.
• Integrate AI with enterprise applications via APIs and standard interfaces.
• Evaluate models for quality, reliability, latency, cost, and security.
• Produce reusable AI components and APIs for end‑to‑end solutions.
• Diagnose AI system behavior with telemetry, traces, and user feedback.
• Collaborate with AI Architecture, Platform Engineering, Data, and cybersecurity teams.

Skills

Python programming

Production‑quality software

Generative AI

API integration

CI/CD / containerization

ML fundamentals

Software development practices

Education

University Degree in CS/AI/ML/Engineering

Advanced Degree in related field

Tools

LangGraph

CrewAI

IBM watsonx

AWS Bedrock

Microsoft AI platforms

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Job description

RTX is seeking an experienced Applied AI Engineer to design, build, evaluate, and deploy production‑grade AI/ML solutions across RTX enterprise systems. You will collaborate with AI architects, platform engineers, data teams, and product groups to move AI from concept to production with measurable business impact.

The role emphasizes hands‑on engineering, understanding AI behavior, failure modes, and building robust, secure AI solutions within a hybrid work environment.