AI Software Architect - RDT Biometrics and Clinical Evidence

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

AI Software Architect - RDT Biometrics and Clinical Evidence

The AI Software Architect - RDT Biometrics and Clinical Evidence will build and lead a high-performing Data & AI Engineering team responsible for translating high-value scientific and business opportunities into trusted, scalable and reusable Data & AI solutions across Biometrics and Clinical Evidence (BCE).

Working in close partnership with data and product teams, the role will bridge deep drug development domain expertise with modern Data & AI engineering — taking solutions from experimentation and proof of concept through industrialisation, deployment and sustained use.

The team will establish engineering practices and reusable capabilities that enable Data & AI and agentic solutions to be developed faster, safely and at scale, while meeting Roche expectations for security, privacy, quality, governance and regulatory compliance. It is expected that this Data & AI Engineer Lead will be responsible for the AI Full Stack (from front-end/UX to back-end services, data & context provisioning through to AI engine & Agentic workflows). Bridging the gap between Data & AI capabilities and full-stack software development, leveraging agentic coding workflows to build scalable, resilient, and intelligent Ai solutions.

You will be responsible for the complete lifecycle—from designing front-end user experiences and resilient back-end APIs to building robust data infrastructure that grounds AI systems in accurate context. As a Data & AI Engineering lead, you will make key decisions regarding tech stack selection, Semantic / Knowledge Graph & Context Layers and performance optimization as Ai solutions evolve from prototype to enterprise-scale production.

Description of the area

The Biometrics and Clinical Evidence team is central to drug development, driving efficiency, safety, and innovation. Our core function is to enable Pharma Development and Research in enhancing clinical trial design and optimizing success by ensuring robust protocols and minimizing risks through simulations, acquiring and managing high quality data—including clinical trial, real-world, imaging, and patient-centric data—to generate evidence, provide actionable insights, and support regulatory filings.

Key capabilities include:

• •AI & Agentic Engineering
• •Software & Platform Engineering
• •Data Engineering & Architecture
• •Semantic, Knowledge & Context Design
• •Responsible AI / regulated engineering
• •Product & business translation
• •Engineering leadership

The opportunity:

• •Build and lead the Data & AI Engineering capability
• •Build, develop and lead a high-performing team of Data & AI Engineers, establishing a strong engineering culture focused on technical excellence, experimentation, reuse, collaboration and measurable impact.
• •Define the capabilities, roles, development pathways and engineering practices required for AI Engineering across BCE and the broader R&D RDT Org..

• •Translate scientific opportunities into scalable Data & AI solutions
• •Partner closely with Product Managers, data teams and PD stakeholders to translate high-value business and scientific problems into technically robust AI solutions.
• •Provide technical leadership from early exploration and proof of concept through architecture, development, deployment, adoption and lifecycle management.
• •Ensure rapid experimentation can transition effectively into production-grade solutions where value is demonstrated.

• •Establish reusable Data & AI and agentic engineering capabilities
• •Define and implement reusable patterns, components and reference architectures for generative AI, retrieval-augmented generation, agentic workflows, knowledge retrieval and AI-enabled applications.
• •Drive a "build once, reuse many" engineering philosophy, reducing duplication and accelerating delivery across use cases.
• •Identify common capabilities emerging across individual solutions and partner with platform teams to industrialise them as shared services where appropriate.

• •Build high-performance Data & AI context and semantic retrieval systems
• •Architect scalable data pipelines, high-throughput vector search indexes, and hybrid retrieval systems explicitly tuned for LLM grounding and retrieval-augmented generation.
• •Define and implement structured schema metadata formats and function-calling protocols to ensure precise, reliable agent tool invocation.
• •Optimize dynamic context windows, chunking strategies, and data freshness to drive accurate model reasoning across complex enterprise domains.

• •Team Member Capability and Career Development 🚀
• •Proactively identifies and develops future-focused engineering team capabilities, aligning with long-term strategic goals.
• •Mentors team members on long-term career paths, creating new opportunities and roles within the organization to support their growth.
• •Addresses and helps resolve complex, systemic roadblocks that impact multiple development teams, and coaches other managers on effective performance management.
• •Oversees the technical and soft skill development, ensuring a balanced and high-performing talent portfolio.

• •Team Health and Sustainability 🌱
• •Establishes and monitors metrics for team health and sustainability across several development teams, implementing strategies to ensure long-term balance.
• •Designs and implements initiatives to improve collaboration and psychological safety across multiple related development teams or a whole product area.
• •Proactively identifies and resolves systemic impediments that affect multiple development teams, improving the overall work environment.
• •Manages the talent composition for own and related teams, planning for future needs and ensuring a healthy pipeline of skills.

Who you are:

Experience

• •8–12+ years relevant technology/software/data experience, with meaningful technical leadership experience with a proven track record of building high performing teams
• •2+ years of experience in the pharmaceutical, biotechnology, or regulated healthcare technology industry

Education

• •Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, Computational Science, Bioinformatics or a related quantitative discipline.
• •Advanced degree preferred but not required where equivalent experience demonstrates deep technical capability.

Technical & business skills

• •Strong practical understanding of modern AI engineering, particularly: Generative AI / LLMs • RAG • agentic architectures • tool use/function calling • orchestration • evaluation • prompt/context engineering • embeddings/vector search • knowledge retrieval • model selection • observability • Fine-tuning/Distillation, Multimodal, High-throughput serving (vLLM)
• •Python • APIs • microservices • cloud architectures • CI/CD • containers • Git • automated testing • infrastructure/platform patterns • identity/access management • observability • DevSecOps • AI FinOps (Token/GPU cost management), Model fallback routing • cloud-native container orchestrations (Kubernetes, AWS/GCP serverless platforms)
• •Data pipelines • semantic models • metadata • APIs • structured/unstructured data • vector databases • knowledge graphs • interoperability • data quality • lineage • provenance
• •Privacy • security • GxP considerations • validation • auditability • traceability • human-in-the-loop • model evaluation • AI risk management
• •Architecture standards • engineering quality • cod