Senior AI Engineer / Developer

Job description

Role PurposeThe 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 ResponsibilitiesAI Solution Build & DeliveryDesign 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 ITDefine 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 DevelopmentBuild 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 ManagementBuild 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 & ComplianceResponsible 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 RequirementsBachelor'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.