Teams at RTX

Defines enterprise AI and machine learning architectures spanning Generative AI, agentic AI, data, applications, cloud, infrastructure, and enterprise systems. Translates business requirements into architecture blueprints, technical strategies, and implementation roadmaps; develops reusable standards and design patterns; evaluates technology alternatives; and leads technical reviews. The role also establishes patterns for MLOps, model serving, monitoring, governance, security, and deployment across cloud, hybrid, on-premises, and restricted environments while mentoring engineering teams.

Design, build, evaluate, and deploy production AI and ML solutions, including generative AI, large language models, retrieval-augmented generation, and agentic workflows. Develop reliable software, APIs, integrations, evaluations, and observability capabilities while partnering with architecture, platform, data, cybersecurity, and business teams. Apply AI engineering practices across enterprise, cloud, hybrid, and regulated environments.

Designs, builds, and operates reusable enterprise AI platform services, backend APIs, model-serving capabilities, agent runtimes, retrieval services, and MLOps infrastructure. The role automates cloud-native deployments across hybrid and restricted environments using Kubernetes, CI/CD, and infrastructure-as-code. Responsibilities include implementing security, identity, observability, lifecycle management, scalability, resilience, and cost visibility for AI workloads while partnering with architecture, application engineering, cybersecurity, data, and product teams.