Senior AI Engineer / Developer

Overview

Design, build, deploy and operate production-grade AI solutions that address prioritized business opportunities across Lonza, working with IT, Data and Platform teams to take solutions from prototype to production.

What you'll do

• 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, 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.
• Define data, infrastructure, network, compute and security requirements needed to deliver AI solutions, and collaborate with IT and Data Engineering teams on data access, provisioning and integration.
• Manage and monitor technology dependencies, risks and timelines that may impact delivery objectives.
• Translate business and solution requirements into scalable technical architectures, partnering with Architecture, Security, Platform Engineering and Data teams to ensure compliance with Lonza standards.
• 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, and contribute them to the Lonza AI platform and tool catalogue.
• Reuse and extend existing AI components and platform capabilities wherever possible to avoid redundant development.
• Build automated CI/CD pipelines and deployment patterns for AI applications, models, prompts and agents.
• Implement observability covering 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 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.
• Incorporate 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.

What you'll need

• 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.

Nice to have

• Experience in life sciences, healthcare, pharmaceuticals or other regulated industries.

Details

• Location: Hyderabad, India.