Senior AI Engineer / Developer

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

• Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, Software Engineering, or a related discipline.
• Demonstrated experience designing and deploying AI, Machine Learning, Generative AI, or Data Science solutions in enterprise environments.
• Hands-on expertise with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agent-based systems, embeddings, orchestration frameworks, and prompt engineering.
• Strong software engineering skills with proficiency in cloud-native architectures, APIs, distributed systems, CI/CD, and Infrastructure-as-Code.
• Experience with MLOps, LLMOps, model deployment, observability, monitoring, and operational support.
• Strong understanding of security, privacy, responsible AI, and AI governance requirements.
• Experience collaborating across business, product, data, and technology teams in agile delivery environments.
• Excellent problem-solving, communication, and stakeholder management skills.
• Experience in life sciences, healthcare, pharmaceuticals, or other regulated industries is advantageous.