AI Engineer

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 At Roche, we are committed to delivering greater benefits to our patients by applying digital, data, and AI capabilities to real-world operational challenges. The AI Engineer Level 6 is a senior hands-on technical contributor within the AI Engineering Cluster, responsible for designing, delivering, evaluating, and operating robust AI solutions across Pharma Technical Operations. This role owns meaningful parts of end-to-end AI delivery, including solution architecture, data pipelines, machine learning and GenAI systems, agentic workflows, AI evaluation harnesses, backend services, user interfaces, and production operations. The role partners with business, IT, quality, and technical experts to translate complex operational challenges into reliable, scalable, compliant, and measurable AI-enabled products. Role description The role is a senior AI engineering role for someone who combines strong technical depth with practical delivery ownership. The AI Engineer Level 6 is expected to independently lead solution delivery, make sound architectural decisions, mentor other engineers, and ensure AI solutions create measurable business value while meeting reliability, scalability, evaluation, agent management, and compliance expectations. AI Solution Leadership & Delivery End-to-end delivery ownership: Own the technical delivery of AI solutions from problem understanding and design through build, deployment, monitoring, evaluation, adoption, and continuous improvement. Solution architecture: Design scalable AI application architectures, including data flows, model integration, GenAI orchestration, APIs, user interfaces, agent workflows, and production infrastructure. GenAI and agentic AI: Design and implement advanced GenAI solutions, including RAG architectures, multi-step workflows, tool-using agents, LLM orchestration, and evaluation approaches. Engineering quality: Apply and help evolve strong engineering practices, including automated testing, code reviews, CI/CD, observability, documentation, and maintainability. Production performance: Ensure AI solutions are reliable, secure, monitored, cost-aware, and continuously improved in production or production-like environments. Value delivery: Translate business needs into technical solutions and help define success metrics that demonstrate operational and business impact. Modern AI Engineering Capabilities Evaluation framework design: 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. AI harness development: Own the development of AI engineering harnesses for repeatable experimentation, evaluation, validation, and comparison of models, prompts, retrieval strategies, agent behaviors, and tool integrations. Agentic AI solution ownership: Design and deliver production-grade agentic AI systems, including agent orchestration, tool integration, task decomposition, state handling, memory patterns, guardrails, and human escalation paths. Agent lifecycle management: Manage deployed agents across their lifecycle, including versioning, testing, release management, monitoring, incident response, continuous improvement, and retirement. Performance and risk monitoring: 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. Model and workflow comparison: Lead structured experiments to compare foundation models, prompts, embeddings, retrieval approaches, orchestration patterns, and agent configurations. Technical Expertise AI engineering depth: Apply strong knowledge of machine learning, predictive modeling, time-series, computer vision and multimodal AI, as well as LLMs, RAG, agentic AI, prompt engineering, model evaluation, and AI application design. Software and platform engineering: Build robust Python-based services, APIs, microservices, dashboards, and integrations using modern software engineering practices. Cloud and DevOps: Design and deploy solutions using AWS, Docker, CI/CD pipelines, monitoring, and cloud or hybrid deployment patterns. Data engineering: Work with SQL, operational data, time-series data, structured and unstructured data, and data pipelines required for AI products. Evaluation methods: Use quantitative metrics, qualitative review, human-in-the-loop assessment, statistical comparison, and production feedback loops to make engineering decisions. Regulated delivery: 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. Forward Deployed AI Engineering Embedded problem solving: 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. Rapid solution development: Move quickly from problem framing to prototypes and production-ready solutions, combining existing platform capabilities, reusable components, and targeted custom engineering. Last-mile integration: Adapt and integrate AI solutions into real operational environments, including relevant data sources, workflows, systems, user interfaces, and business processes. User-driven iteration: Work closely with end users to observe solution performance, collect feedback, measure value, and continuously improve usability, quality, and business impact. Scale through reuse: Turn learnings from individual deployments into reusable patterns, tools, components, and platform capabilities that can be applied across Pharma Technical Operations. Technical Leadership & Mentoring Engineering mentorship: Coach less experienced engineers on AI engineering, software development, evaluation practices, agentic systems, cloud deployment, and production operations. Best practices: Contribute to shared standards, reusable patterns, and engineering guidelines for the AI Engineering Cluster. Technical problem-solving: Structure ambiguous technical problems, evaluate solution options, and guide teams toward pragmatic, high-impact outcomes. Capability building: Help build AI engineering capability across Pharma Technical Operations through knowledge sharing, examples, and hands-on support. Collaboration, Adoption & AI Advocacy Stakeholder partnership: Partner with process experts, business stakeholders, IT, quality, and other digital teams to identify, design, implement, evaluate, and sustain AI-enabled improvements. Organizational alignment: Communicate technical concepts clearly to both technical and non-technical audiences, supporting alignment and adoption. AI advocacy: Promote effective, responsible, and value-driven use of AI within Pharma Technical Operations. Continuous lear