AI Platform Engineer

RTX•Farmington, CT

2d•Hybrid

About The Position

This position is part of the Enterprise Services – Data & AI organization within RTX Corporate, supporting the company’s mission to develop and scale AI capabilities responsibly and securely across the enterprise. The AI Platform Engineer will design and implement the software and infrastructure that enable scalable and secure AI applications across RTX. The role focuses on developing APIs, orchestration services, and observability capabilities that connect AI models and intelligent systems with enterprise data and services.

Requirements

Typically requires a University Degree or equivalent experience and minimum 10 years prior relevant experience, or an Advanced Degree in a related field and minimum 7 years experience
Python APIs; cloud AI/ML
Microservices, Docker/Kubernetes
Messaging pipelines; monitoring/logging

Nice To Haves

Experience implementing observability stacks (OpenTelemetry, Prometheus, Grafana, MLflow).
Experience designing and scaling enterprise-grade AI infrastructure that is highly performant, cost-efficient, and optimized for large-scale data and model operations.
Knowledge of CI/CD automation, DevSecOps, and infrastructure-as-code.
Hands-on experience building and deploying agents for business use cases (e.g., workflow orchestration, data transformation or analysis, multi-agent system)
Background in regulated or large-scale enterprise environments (aerospace, defense, or finance).

Responsibilities

Design, build, and maintain APIs and orchestration services that support AI and automation applications.
Integrate cloud and on-premises AI services (Azure AI, Databricks, AWS Bedrock, Anthropic, etc.).
Develop telemetry and observability components using OpenTelemetry and MLflow.
Containerize and deploy microservices using Docker and Kubernetes.
Collaborate with Infrastructure, Security, and Compliance teams to meet enterprise and regulatory standards.
Design and build scalable, reliable, and cost-efficient platform services that enable seamless integration, automation, and high performance across development and production environments.
Identify areas for improvement and implement new tools or technologies to enhance AI infrastructure efficiency, reliability, and security.