AI Engineer

Join our team as an AI Engineer Level 6, where you will lead the delivery of advanced AI solutions within Pharma Technical Operations. 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.

At Roche, we are committed to delivering greater benefits to our patients by applying digital, data, and AI capabilities to real-world operational challenges.

• Own the technical delivery of AI solutions from problem understanding and design through build, deployment, monitoring, evaluation, adoption, and continuous improvement.
• Design scalable AI application architectures, including data flows, model integration, GenAI orchestration, APIs, user interfaces, agent workflows, and production infrastructure.
• Design and implement advanced GenAI solutions, including RAG architectures, multi-step workflows, tool-using agents, LLM orchestration, and evaluation approaches.
• Apply and help evolve strong engineering practices, including automated testing, code reviews, CI/CD, observability, documentation, and maintainability.
• Ensure AI solutions are reliable, secure, monitored, cost-aware, and continuously improved in production or production-like environments.
• Translate business needs into technical solutions and help define success metrics that demonstrate operational and business impact.
• Design and implement data science-driven evaluation frameworks for AI, GenAI, RAG, and agentic AI systems, including offline benchmarks, golden datasets, synthetic test cases, human evaluation workflows, and automated regression testing.
• Own the development of AI engineering harnesses for repeatable experimentation, evaluation, validation, and comparison of models, prompts, retrieval strategies, agent behaviors, and tool integrations.
• Design and deliver production-grade agentic AI systems, including agent orchestration, tool integration, task decomposition, state handling, memory patterns, guardrails, and human escalation paths.
• Manage deployed agents across their lifecycle, including versioning, testing, release management, monitoring, incident response, continuous improvement, and retirement.
• Implement and operate observability for AI systems, including quality metrics, business impact metrics, cost, latency, failure modes, drift, hallucination risk, grounding quality, and operational reliability.
• Lead structured experiments to compare foundation models, prompts, embeddings, retrieval approaches, orchestration patterns, and agent configurations.
• Build robust Python-based services, APIs, microservices, dashboards, and integrations using modern software engineering practices.
• Design and deploy solutions using AWS, Docker, CI/CD pipelines, monitoring, and cloud or hybrid deployment patterns.
• Work with SQL, operational data, time-series data, structured and unstructured data, and data pipelines required for AI products.
• Use quantitative metrics, qualitative review, human-in-the-loop assessment, statistical comparison, and production feedback loops to make engineering decisions.
• Partner with IT and quality stakeholders to ensure solutions are designed with appropriate compliance, validation, security, privacy, data governance, responsible AI, traceability, documentation, and lifecycle controls.
• Work directly with business, process, site, and technical teams to understand operational challenges, translate ambiguous needs into concrete AI opportunities, and co-design practical solutions.
• Move quickly from problem framing to prototypes and production-ready solutions, combining existing platform capabilities, reusable components, and targeted custom engineering.
• Adapt and integrate AI solutions into real operational environments, including relevant data sources, workflows, systems, user interfaces, and business processes.
• Work closely with end users to observe solution performance, collect feedback, measure value, and continuously improve usability, quality, and business impact.
• Turn learnings from individual deployments into reusable patterns, tools, components, and platform capabilities that can be applied across Pharma Technical Operations.
• Coach less experienced engineers on AI engineering, software development, evaluation practices, agentic systems, cloud deployment, and production operations.
• Contribute to shared standards, reusable patterns, and engineering guidelines for the AI Engineering Cluster.
• Structure ambiguous technical problems, evaluate solution options, and guide teams toward pragmatic, high-impact outcomes.
• Help build AI engineering capability across Pharma Technical Operations through knowledge sharing, examples, and hands-on support.
• Partner with process experts, business stakeholders, IT, quality, and other digital teams to identify, design, implement, evaluate, and sustain AI-enabled improvements.
• Communicate technical concepts clearly to both technical and non-technical audiences, supporting alignment and adoption.
• Promote effective, responsible, and value-driven use of AI within Pharma Technical Operations.
• Stay current with advances in AI, GenAI, agentic systems, evaluation science, software engineering, and cloud technologies, and translate relevant innovations into practical Roche use cases.