# Associate Director, AI Engineer at Novartis

Novartis is hiring an Associate Director, AI Engineer in Prague, Czechia. Level rates it Builds AI ●●●●; you can [apply on Novartis](https://jobsbylevel.com/go/5070972c-d9ac-4968-b6c3-3c8b050f8104?utm_medium=ai-agent).

AI Level 4, AI centrality 95 out of 100. Prague.

## Details

- Company: [Novartis](https://jobsbylevel.com/companies/novartis)
- AI level: AI Level 4 (score 95 out of 100)
- Location: Prague
- Posted: November 3, 2026
- Apply: https://jobsbylevel.com/go/5070972c-d9ac-4968-b6c3-3c8b050f8104?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=associate-director-ai-engineer-at-novartis-b4d235

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## Description

Salary Range: Kč1,354,220.00 - Kč2,514,980.00 Job Description Summary #LI-Hybrid Location: Prague, Czech Republic / Basel, Switzerland About the Role We are hiring four Associate Directors, AI Engineering to join Biomedical Research Data & Digital, Software & Workflows and help shape the next generation of agentic AI capabilities across Novartis Biomedical Research. As an Associate Director, AI Engineer, you will be a senior individual contributor and technical authority for production-grade agentic AI. This is a hands-on technical leadership role, not a people-management position. Leadership is exercised through technical direction, architecture, engineering excellence and influence rather than direct reports. You will own complex architecture and design decisions, establish engineering standards and reference architectures for others to build upon, and remain actively involved in the code. The Software & Workflows organization partners with scientists, data experts and AI specialists across Novartis Biomedical Research to transform scientific workflows and laboratory processes. Our ambition is to make advanced digital and AI capabilities usable, scalable and impactful, helping teams generate better insights, improve decisions and accelerate the discovery of innovative medicines. You will set the technical direction for agentic AI across a broad portfolio of software delivery initiatives. You will bring together engineering, science, architecture, operations, security and enterprise technology, challenge established approaches, make thoughtful technical trade-offs and help engineers learn and grow, while ensuring solutions meet demanding standards for quality, performance, security, compliance and reliability. What You'll Work On You will work on agentic AI for drug discovery, scientific workflow automation and lab-in-the-loop systems. You will own the architecture behind some of the team's most complex technical challenges, including multi-agent platforms designed to accelerate target identification and the broader drug discovery pipeline, agentic automation of complex cross-functional scientific research workflows, and lab-in-the-loop systems integrating AI reasoning with wet-lab experimentation. You will establish the engineering patterns and reference architectures that enable these systems to be scalable, trustworthy and reusable across the organization. Job Description Key Responsibilities Own the architecture and design of agentic AI platforms and solutions end to end, remaining hands-on through development, deployment, monitoring, reliability and continuous improvement. Take complex technical problems from ambiguous business or scientific needs through to robust, production-grade solutions. Drive the transformation of existing products, processes and scientific workflows into scalable agentic AI-enabled capabilities, including drug discovery, scientific workflow automation and lab-in-the-loop systems. Own architecture decisions for agentic systems and large language model inference at scale on AWS, including Amazon Bedrock and AgentCore, balancing reliability, latency, cost, security and governance. Establish engineering best practices and reference architectures for AI system performance, scalability, security, governance and maintainability. Define and advance DevOps, MLOps and AgentOps standards, including CI/CD, infrastructure as code, containerization, reproducible pipelines, model/data/prompt versioning and experiment tracking. Guide the evaluation, integration and optimization of AI models, agent orchestration frameworks and supporting platform components. Define evaluation and observability standards, including agent test sets, human or LLM-based evaluation, tracing, and cost and latency monitoring. Partner with product, data, architecture, security and scientific stakeholders to define technical roadmaps, platform standards and solution priorities aligned with business and scientific goals. Raise the

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