<h2>About the role:</h2> <p>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.</p> <p>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.</p> <p>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.</p> <h2>What you'll do:</h2> <ul> <li><strong>Lead applied AI solution design and architecture,</strong> breaking down ambiguous business problems into concrete, actionable AI solution designs.</li> <li><strong>Contribute to the detailed design of large-scale, distributed AI/ML systems,</strong> ensuring performance, reliability, and security.</li> <li><strong>Design and improve retrieval, prompting, tool-calling, and orchestration patterns</strong> for internal use cases such as knowledge assistants, workflow automation, and decision support.</li> <li><strong>Champion and enforce design standards, patterns, and best practices</strong> for scalable and secure development of AI applications across teams.</li> <li><strong>Drive the hands-on development and implementation</strong> of key AI components, supporting both traditional and Generative AI model development and deployment.</li> <li><strong>Build and deploy AI-enabled internal workflows</strong> that connect enterprise systems, data sources, model APIs, and automation layers.</li> <li><strong>Leverage AI-assisted development to accelerate implementation,</strong> bringing a strong 'editor' mindset to ruthlessly audit, review, and secure AI-generated code against our standards for reliability, security, observability, and documentation.</li> <li><strong>Lead the implementation and continuous improvement of MLOps pipelines,</strong> including automated model training, versioning, deployment, and monitoring.</li> <li><strong>Develop and maintain evaluation approaches</strong> for output quality, retrieval accuracy, latency, and failure modes, and use findings to improve system performance over time.</li> <li><strong>Help identify where human review, controls, and escalation paths are required</strong> to support responsible deployment of AI-enabled systems.</li> <li><strong>Apply sound engineering judgment</strong> to balance speed, usability, risk, and maintainability in production and near-production environments.</li> <li><strong>Proactively collaborate with executive leadership, data science, engineering, and product stakeholders</strong> to translate business use-cases into scalable AI solutions.</li> <li><strong>Lead rigorous problem formulation</strong> 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.</li> <li><strong>Provide technical leadership and mentorship</strong> to other AI/ML engineers, fostering a culture of engineering excellence and hands-on experimentation.</li> <li><strong>Contribute reusable components, playbooks, and patterns</strong> that reduce duplicate effort and improve speed across the engineering environment.</li> <li><strong>Technical architecture and AI implementations directly to measurable business outcomes,</strong> recognizing that technology serves the business.</li> <li><strong>Actively research and evaluate cutting-edge AI/ML techniques,</strong> algorithms, and models to identify opportunities for platform enhancement and new solution development.</li> </ul> <h2>Experience you'll need:</h2> <ul> <li>10+ years of core Software/Systems Engineering architecture experience, including 4+ years dedicated to building and deploying applied AI/ML systems in production.</li> <li>Bachelor’s degree in Computer Science, Engineering, or a related field (Master’s/Ph.D. is a plus).</li> <li>Direct hands-on experience with Generative AI technologies, including LLMs, Retrieval-Augmented Generation (RAG), context graphs, memory management, and agentic AI architectures.</li> <li>Strong programming skills in Python, Java, or Go. Deep proficiency with GenAI orchestration frameworks (e.g., LangGraph, LlamaIndex, AutoGen, Semantic Kernel) and a working knowledge of traditional ML frameworks (e.g., PyTorch, TensorFlow).</li> <li>Extensive hands-on experience with the modern AI data ecosystem, including Vector Databases (e.g., Pinecone, Weaviate) and Graph Databases (e.g., Neo4j).</li> <li>Experience with distributed systems architecture, event-driven streaming platforms, and cloud AI/ML platforms (e.g., GCP Vertex AI, AWS SageMaker, Azure OpenAI).</li> <li>Strong ability to work across data, model, application, and infrastructure layers, operating effectively with incomplete