About the role

At Rubrik IT, we are transforming how the enterprise thinks and operates — building a "thinking enterprise" where intelligent, agentic systems reason across our data, tools, and workflows to drive speed, quality, and measurable business impact. As an AI Architect, you will be a pivotal technical leader driving AI Native solutions that make this vision real.

You will operate where AI innovation, top-tier engineering, and business outcomes converge, acting as the technical bridge between complex business problems and cutting-edge AI execution. Leveraging our robust Enterprise AI Platform, you will translate ambiguous problems into concrete AI solution designs and ensure their successful deployment and measurable impact.

This role is designed for systems-thinkers and orchestrators with an AI-first mindset — leaders who move fluidly between hands-on building AI solutions, and who know how to harness model APIs, data sources, and workflow layers to build practical technology that serves the business.

What you'll do

Lead applied AI solution design and architecture, breaking down ambiguous business problems into concrete, actionable AI solution designs.
Contribute to the detailed design of large-scale, distributed AI/ML systems, ensuring performance, reliability, and security.
Design and improve retrieval, prompting, tool-calling, and orchestration patterns for internal use cases such as knowledge assistants, workflow automation, and decision support.
Champion and enforce design standards, patterns, and best practices for scalable and secure development of AI applications across teams.
Drive the hands-on development and implementation of key AI components, supporting both traditional and Generative AI model development and deployment.
Build and deploy AI-enabled internal workflows that connect enterprise systems, data sources, model APIs, and automation layers.
Leverage AI-assisted development to accelerate implementation, bringing a strong 'editor' mindset to ruthlessly audit, review, and secure AI-generated code against our standards for reliability, security, observability, and documentation.
Lead the implementation and continuous improvement of MLOps pipelines, including automated model training, versioning, deployment, and monitoring.
Develop and maintain evaluation approaches for output quality, retrieval accuracy, latency, and failure modes, and use findings to improve system performance over time.
Help identify where human review, controls, and escalation paths are required to support responsible deployment of AI-enabled systems.
Apply sound engineering judgment to balance speed, usability, risk, and maintainability in production and near-production environments.
Proactively collaborate with executive leadership, data science, engineering, and product stakeholders to translate business use-cases into scalable AI solutions.
Lead rigorous problem formulation by partnering with R&D,, Security, Data, HR, Finance, and business stakeholders — ensuring we apply AI to the right problems before translating needs into scalable technical solutions.
Provide technical leadership and mentorship to other AI/ML engineers, fostering a culture of engineering excellence and hands-on experimentation.
Contribute reusable components, playbooks, and patterns that reduce duplicate effort and improve speed across the engineering environment.
Technical architecture and AI implementations directly to measurable business outcomes, recognizing that technology serves the business.
Actively research and evaluate cutting-edge AI/ML techniques, algorithms, and models to identify opportunities for platform enhancement and new solution development.

Experience you'll need

10+ years of core Software/Systems Engineering architecture experience, including 4+ years dedicated to building and deploying applied AI/ML systems in production.
Bachelor’s degree in Computer Science, Engineering, or a related field (Master’s/Ph.D. is a plus).
Direct hands-on experience with Generative AI technologies, including LLMs, Retrieval-Augmented Generation (RAG), context graphs, memory management, and agentic AI architectures.