Responsibilities
- Design and build end-to-end AI applications, agents, services, APIs, integrations, data pipelines, and user-facing components.
- Apply prompting, model selection, optimization, LLM evaluation, Retrieval-Augmented Generation, embeddings, tool-calling, and agentic AI techniques.
- Build and orchestrate AI agents that interact with enterprise systems, applications, and data with appropriate controls, traceability, and human oversight.
- Engineer solutions for scalability, reliability, performance, maintainability, security, and cost efficiency.
- Develop automated testing and evaluation frameworks for functionality, model behavior, integrations, security controls, and regressions.
- Own solutions from proof of concept through deployment, stabilization, and operational support.
- Define data, infrastructure, network, compute, and security requirements and collaborate with IT, Data Engineering, Architecture, Security, Platform Engineering, and Data teams.
- Build reusable AI engineering assets, services, templates, frameworks, accelerators, and platform capabilities.
- Build and maintain MLOps and LLMOps pipelines for deployment automation, monitoring, and observability.
- Implement observability for application health, model and agent performance, latency, infrastructure utilization, token consumption, quality metrics, and operational issues.
- Apply version control and lifecycle management across code, models, prompts, agent configurations, and evaluation datasets.
- Develop cloud-native solutions using containers, APIs, Infrastructure-as-Code, and managed cloud services.
- Implement responsible AI, identity and access management, secrets management, data protection, guardrails, governance, traceability, explainability, and auditability controls.
- Validate solutions against security, privacy, quality, regulatory, and GxP requirements and troubleshoot production issues.
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, Retrieval-Augmented Generation, agent-based systems, embeddings, orchestration frameworks, and prompt engineering.
- Strong software engineering skills 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.
Categories
About Lonza
Lonza is a pioneer and world leader in the CDMO industry, setting the pace with cutting-edge science, smart technology, and lean manufacturing. United by a common purpose, we turn our customers’ breakthrough innovations into viable therapies and manufacture the medicines of tomorrow. Founded in Switzerland in 1897, our company is the first and original CDMO, and today we are more dynamic, agile and forward-focused than ever before. As one of the largest Western CDMOs, we bring together a global team of around 18,500 colleagues across five continents to deliver comprehensive, integrated solutions for our customers' complex needs. For our customers and their patients, we bring quality, safety and reliability to bring life-enhancing and life-saving treatments to market at speed and at scale. For our colleagues, we strive to create a sense of belonging and inclusion where every person can thrive and bring their best.