AI Solutiuons Architect

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters. The Position About the Role At Roche Digital Technology, we are advancing the boundaries of Applied AI. As part of our strategic initiatives, we are enabling high-value AI (with a strong focus on Generative AI) through fit-for-purpose platforms, services and applications. The Applied AI Use Case Engineering & Operations Team is tasked with building innovative AI applications, genAI agents and agentic foundations. In the 2026 tech landscape, the lines between traditional disciplines have blurred. We operate in small agile teams (e.g., ~9 members) powered by advanced coding agents (like Claude Code) to develop and ship solutions faster than ever before. We are looking for a highly skilled, deeply hands-on AI Software Architect to design and help implement end-to-end GenAI and non-Gen AI applications, with a particular focus on agentic systems. You will not simply be drawing diagrams; you will be expected to remain deeply entrenched in the code, acting as a full-stack technical anchor whose workload for rigorous code review and architecture enforcement is critical to our high-velocity teams. Core Tech Stack & Scope This role will involve working deeply with multimodal foundation models, Retrieval-Augmented Generation (RAG) pipelines, advanced agentic workflows, vector, graph and traditional databases, APIs, MCPs, A2A, MLOps subsystems, new emerging technologies and other solution components. You will utilize cloud services (AWS and multicloud) to develop scalable and robust AI solutions. Beyond AI components, the architect must ensure that the entire system's software ecosystem - from frontend and backend services to data pipelines, integrations, authentication, observability, and infrastructure - is well-designed, scalable, and maintainable. Key Responsibilities End-to-End System Architecture & Hands-On Engineering: Define and design AI (with focus on genAI and agentic) and non-AI components of software systems, ensuring modularity, scalability, and security. Architect solutions that integrate AI capabilities into enterprise systems while ensuring seamless interoperability with backend services, APIs, MCPs, databases, and user interfaces. Ensure adherence to cloud-native best practices across AWS and multicloud deployments, including containerization, orchestration, and infrastructure as code. Evaluate the best fit for purpose technologies. Ensure newly developed AI applications fit within the context of particular RDT and Business functions, and that they strictly follow architectural patterns and standards used across RDT. Agentic AI Model Integration & Optimization: Define robust patterns for utilizing LLMs, multimodal models, and RAG, agentic systems and other emerging AI technologies. Design efficient model-serving pipelines, integrating AI capabilities into existing business workflows. Full-Stack Enterprise Engineering & Code Quality: Ensure that AI applications follow software engineering best practices, including version control, CI/CD, MLOps, automated testing, and code quality assurance. Conduct rigorous code reviews for agent-assisted and human-generated code to maintain high enterprise standards. Architect secure and scalable integrations to facilitate seamless communication between AI models, databases, and user interfaces. Design data pipelines that efficiently handle structured and unstructured data, ensuring AI models receive high-quality input data. Integrate identity management and authentication mechanisms to ensure secure access to AI applications. Be ready to jump into the code to be a hands-on partner within solutions development teams. Observability, Performance & Security: Define monitoring and logging strategies for AI-driven applications to ensure model performance, API/MCP health, and data integrity. Implement AI observability practices, ensuring visibility into application behaviors and anomaly identification. Design architectures that adhere to data governance, security, compliance, and ethical AI guidelines. Collaboration & Governance: Work closely with AI Engineers, Software Engineers, Product Owners and other members of Agile development teams to translate business requirements into AI-driven architectures. Provide technical leadership in AI architecture reviews, design discussions, and solution validation. Collaborate with Agile teams and key stakeholders across the organization to consult on, design and implement AI systems that meet Roche's architectural standards and adhere to AI governance guidelines. Practical Skills Required Experience: 7+ years of experience in software architecture and engineering, including at least 3 years in AI-related projects. Proven track record of designing and deploying large-scale, cloud-based AI and non-AI systems. Proven experience leveraging AI coding agents to accelerate full-stack development cycles. Skills Must-have: Architecting production-level AI systems including RAGs, Vector DBs, MCP, end-users, integrations, etc., but also observability, DevOps, durable and available system designs. AI Expertise: Deep understanding of various ML algorithms, model training techniques, and evaluation metrics, foundational models utilization and integration, agentic systems design and engineering. Cloud Platforms: Expertise in AWS and multicloud services, serverless computing, and other cloud services required to build AI applications end-to-end, from IaC with Terraform to exposing securely production-level applications over the network, understanding of hardware and software infrastructure needed to support AI workloads. Databases: Strong architectural knowledge of vector databases (e.g. AWS OpenSearch, Azure AI search), Snowflake, SQL, NoSQL, event-driven and graph architectures. Building resilient , highly available and secure IT systems. Security & Compliance: Strong understanding of software solutions security and compliance requirements. Stakeholder management and communication : Influence your colleagues, but also non-technical Senior Stakeholders on designs you create. Programming: Advanced proficiency in Python with strong experience in backend development, coupled with a strong command of modern frontend ecosystems. Software Engineering Be