Cloud Platform Architect

About the role

As a Cloud Platform Architect within our Cloud Engineering organization, you will play a critical role in designing, building, and maintaining our cloud infrastructure and platform services. You will collaborate with product engineering teams to ensure that our applications are built on a scalable, reliable, and efficient cloud foundation. As we accelerate our investment in AI and agentic workloads, you will play a pivotal role in shaping the foundational infrastructure that powers intelligent, autonomous systems at scale. This is a high-impact, high-visibility position that sits at the intersection of platform engineering, AI infrastructure, and enterprise architecture. About Intapp: Intapp, based in Silicon Valley, is a leader in Vertical AI SaaS company, collaborating with over 2,550 professional and financial services firms globally. With 20+ years of industry expertise, Intapp's "Intelligence Applied" approach is transforming how businesses operate and leverages a strategic partner with Microsoft in delivering industry cloud solutions. The company culture promotes accountability, responsibility, and growth in a diverse, inclusive, and collaborative environment. Since going public in 2021, Intapp has expanded to over 1,600 employees globally with development centers in Palo Alto, Charlotte, Belfast, Berlin, and Lisbon.

Responsibilities

• Platform Design & Architecture
• Take ownership of the cloud platform architecture used by product teams, ensuring it meets their needs and integrates seamlessly with their applications.
• Lead the design and architecture of cloud platform components purpose-built to support AI and agentic workloads, including model serving infrastructure, vector databases, orchestration frameworks, and agent runtime environments.
• Collaborate with product engineering teams to understand their requirements and translate them into robust, scalable platform architectural solutions.
• Oversee the integration of new features and capabilities into the cloud platform, maintaining a focus on performance, cost efficiency, security, and scalability.
• AI & Agentic Platform Engineering
• Architect cloud infrastructure to support large-scale AI/ML model training, fine-tuning, and inference workloads.
• Collaborate with AI/ML engineering teams to define infrastructure requirements for retrieval-augmented generation (RAG) architectures, vector database integrations, and LLM deployment patterns.
• Evaluate and implement AI-specific cloud services and frameworks, including Azure AI, Azure Machine Learning, and relevant open-source tooling.
• Technology Evaluation & Selection
• Continuously research and evaluate new cloud technologies and tools - with particular focus on emerging AI and agentic computing paradigms - to identify opportunities for improvement.
• Make evidence-based recommendations for technology adoption aligned with strategic goals and engineering principles.
• Collaboration & Technical Leadership
• Partner closely with product engineering teams through design reviews, architecture review boards, and sprint planning to guide best-practice cloud and AI architecture decisions.
• Review and refine existing architectures to align with evolving business needs, technological advancements, and AI strategy.
• Mentor and coach engineers across the organization on cloud architecture principles, AI infrastructure patterns, and platform best practices.
• Drive organization-wide architecture standards and serve as a key voice in shaping the company's cloud and AI platform roadmap.

Requirements

• Education & Experience
• Bachelor's degree in computer science, Engineering, or a related field (or equivalent practical experience).
• 8+ years of experience in cloud architecture.
• Proven track record of designing and implementing large-scale cloud solutions in production environments.
• Hands-on experience with AI/ML infrastructure, including model serving, training pipelines, and agentic system design.
• Technical Skills
• Deep understanding of cloud architecture principles, design patterns, and best practices at enterprise scale.
• Expertise in Azure cloud services and architecture, including AKS, Azure AI, Azure Machine Learning, and Azure OpenAI Service.
• Proficiency in Kubernetes orchestration, containerization, and microservices architecture.
• Strong knowledge of relational databases (PostgreSQL, MSSQL) and familiarity with vector databases (e.g., Pinecone, Weaviate, Azure AI Search).
• Experience with agentic AI frameworks such as LangChain, AutoGen, or Semantic Kernel.
• Familiarity with LLM deployment patterns, including inference optimization, fine-tuning, and RAG architectures.
• Experience with serverless computing, event-driven architectures, and related frameworks.
• Proficiency across Linux and Windows server environments.
• Soft Skills
• Excellent communication and collaboration skills, with the ability to work effectively across engineering, product, and executive stakeholders.
• Strong problem-so

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