# Specialist, AI Engineer at Novartis

Novartis is hiring a Specialist, AI Engineer in Prague, Czechia. Level rates it Builds AI ●●●●; you can [apply on Novartis](https://jobsbylevel.com/go/04c35d08-42fe-412f-ab4a-da07e5c41b17?utm_medium=ai-agent).

AI Level 4, AI centrality 97 out of 100. Prague.

## Details

- Company: [Novartis](https://jobsbylevel.com/companies/novartis)
- AI level: AI Level 4 (score 97 out of 100)
- Location: Prague
- Posted: November 13, 2026
- Apply: https://jobsbylevel.com/go/04c35d08-42fe-412f-ab4a-da07e5c41b17?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=specialist-ai-engineer-at-novartis-f21f54

## Prepare for this job

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## Description

Salary Range: Kč750,050.00 - Kč1,392,950.00 Job Description Summary Location: Prague, Czech Republic Working model: Hybrid #LI-Hybrid About the role 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. As a Specialist AI Engineer in BR Data & Digital, Software & Workflows, you will build and ship production-ready generative and agentic AI solutions that accelerate biomedical research. Working on AWS, you will implement LLM-powered applications and agents, including RAG pipelines, tool-using agents, APIs and supporting backend services. You will write clean, well-tested code under the guidance of senior engineers, use modern AI coding tools day to day, and help operationalize responsible, reliable AI. This is a role for a curious, learning-oriented engineer who wants to build hands-on depth in modern and AI-enabled engineering practices while contributing to solutions that meet high standards for quality, performance, security, compliance and reliability. What you will work on You will work on agentic AI for drug discovery, scientific workflow automation and lab-in-the-loop systems. This includes building the agents, tools and services that help scientists find targets faster, automate routine research workflows and support the connection between AI predictions and wet-lab experiments. Job Description Key responsibilities Build production-ready generative and agentic AI features, including LLM applications and agents on AWS, owning them from implementation through testing and deployment. Implement LLM inference pipelines, including prompting, retrieval-augmented generation, tool and function calling, and structured output. Develop AI agents using agentic frameworks such as Strands, LangGraph, LangChain, CrewAI or similar, and integrate them into existing applications and workflows. Build secure APIs and containerized backend services for models, agents and tools. Contribute to evaluation and observability practices, including test sets, tracing, and monitoring of cost and latency. Follow good ML and MLOps practices, including reproducible pipelines, data, model and prompt versioning, and systematic evaluation. Apply DevOps fundamentals, including version control, automated testing, code review, containerization and CI/CD. Use AI coding tools, such as Claude Code, GitHub Copilot or similar, to improve throughput and code quality. Collaborate with engineers, scientists, data experts and AI specialists to deliver reliable and scalable AI-enabled solutions. Help ensure solutions meet required standards for quality, performance, security, compliance and reliability. Essential requirements 3+ years of software engineering experience, including recent hands-on work building LLM-powered or ML applications. Strong Python skills and solid software engineering fundamentals, including testing, version control and CI/CD. Familiarity with DevOps and MLOps practices, including containers, CI/CD and reproducible, well-tested pipelines. Hands-on experience with LLM inference, including prompting, RAG, and tool use or function calling. Experience building and deploying services on AWS. Familiarity with at least one agentic framework, such as Strands, LangGraph, LangChain, CrewAI or equivalent. Ability to work effectively in a collaborative, multidisciplinary environment. Curiosity, learning orientation and a practical approach to solving complex technical problems. Desirable requirements Exposure to Amazon Bedrock and/or Bedrock AgentCore. Experience with vector stores such as OpenSearch, pgvector or similar, and embeddings. Familiarity with AI coding assistants in a professional workflow. Experience in a

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