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

• Build automated CI/CD pipelines and deployment patterns for AI applications, models, prompts and agents.
• Implement observability frameworks covering application health, model and agent performance, latency, infrastructure utilization, token consumption, quality metrics, and operational issues.
• Apply version control and lifecycle management practices across code, models, prompts, agent configurations, and evaluation datasets.
• Develop solutions using cloud-native engineering principles, including containers, APIs, Infrastructure-as-Code, and managed cloud services.
• Troubleshoot production issues and continuously improve solution reliability, performance, scalability, and operating costs.

Security, Responsible AI & Compliance

• Responsible AI principles into solution design, including identity and access management, secrets management, and data protection controls.
• Implement AI guardrails and governance mechanisms to ensure safe, compliant, and trustworthy model and agent behavior.
• Ensure traceability, explainability, and auditability of AI-generated outputs, key decisions, and system interactions as required by business and regulatory standards.
• Validate AI solutions against security, privacy, quality, and regulatory requirements, particularly within regulated and GxP-relevant environments.
• Collaborate with Security, Privacy, Quality, Compliance, and Responsible AI stakeholders to mitigate risks and ensure production readiness.

Key Requirements

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