RTX Corporation is looking to design, deploy, and optimize scalable AI and machine learning solutions on cloud platforms to power advanced AI models and analytics capabilities, driving innovation and supporting the company's mission.
Requirements
- Certification in AWS or Azure and their AI/ML services.
- Strong understanding of MLOps principles and tools.
- Experience with containerization (Docker) and orchestration (Kubernetes).
- Proficiency in programming languages such as Python, Java, or C++.
- Experience with generative & agentic AI technologies and frameworks.
- Familiarity with infrastructure-as-code tools such as Terraform or CloudFormation.
- SAFe devops or similar certification
Responsibilities
- Architect, implement, and manage cloud-based solutions to support AI and machine learning workloads across platforms such as Azure, AWS, or GCP.
- Optimize cloud resources for scalability, performance, and cost-efficiency while ensuring robust security and compliance measures.
- Collaborate with data scientists and engineers to deploy machine learning models in production and maintain MLOps pipelines for training, testing, deployment, and monitoring.
- Utilize cloud-native tools and services like Azure Machine Learning, AWS SageMaker to deliver AI solutions.
- Leverage containerization (Docker) and orchestration (Kubernetes) technologies for efficient deployment and scaling of AI solutions.
- Work closely with cross-functional teams, including Data Engineering, Architecture, and Security, to align cloud strategies with organizational goals.
- Monitor and manage cloud infrastructure to maintain reliability, performance, and security while driving cost optimization initiatives, and adhering to RTX policy updates.
Other
- This job requires a U.S. Person.
- Bachelor’s degree in Computer Science, Engineering, or a related field and 10+ years of experience in cloud engineering or AI/ML solution development.
- Master’s degree in a relevant field.
- Additional soft skills: communication, problem-solving &collaboration.
- 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.
