Senior AI Engineer / Developer

Role Purpose The AI Engineer is responsible for designing, building, deploying, and operating production-grade AI solutions that address prioritized business opportunities across Lonza. Working closely with cross-functional teams, including IT, Data, and Platform teams, this role delivers scalable AI applications, agents, and integrations from prototype through production. The AI Engineer applies modern AI/ML, software engineering, and cloud-native practices while contributing reusable components, patterns, and capabilities back into the Lonza AI platform to accelerate enterprise-wide adoption and delivery of AI solutions. Key Responsibilities AI Solution Build & Delivery Design and build end-to-end AI applications, agents, and services for prioritized business opportunities, including APIs, integrations, data pipelines, and user-facing components where required. Apply and continuously refine prompting, model selection, and optimization techniques, leveraging LLM evaluation frameworks, Retrieval-Augmented Generation (RAG), embeddings, tool-calling frameworks, and agentic AI architectures. Build and orchestrate AI agents that interact with enterprise systems, applications, and data while ensuring appropriate controls, traceability, and human oversight. Engineer AI solutions for scalability, reliability, performance, maintainability, and cost efficiency, beyond functional accuracy alone. Develop automated testing and evaluation frameworks covering application functionality, AI model behavior, integrations, security controls, and regression testing. Take technical ownership of solutions from proof-of-concept through deployment, stabilization, and transition into operational support. Technical Partnership with IT Define data, infrastructure, network, compute, and security requirements required to successfully deliver AI solutions. Collaborate with IT and Data Engineering teams on data access, provisioning, and integration requirements throughout the development lifecycle. Manage and monitor technology dependencies, risks, and timelines that may impact delivery objectives. Translate business and solution requirements into scalable technical architectures while partnering with Architecture, Security, Platform Engineering, and Data teams to ensure compliance with Lonza standards. Platform & Reusable Capability Development Build and maintain MLOps and LLMOps pipelines, including deployment automation, monitoring, and observability for AI solutions. Develop reusable AI engineering assets, services, templates, frameworks, and accelerators that can be leveraged across multiple use cases. Contribute reusable capabilities into the Lonza AI platform and tool catalogue to accelerate future AI product delivery. Reuse and extend existing AI components and platform capabilities wherever possible to drive consistency and avoid redundant development efforts. Engineering Operations & AI Lifecycle Management