1. What is an AI Engineer at Novartis?

As an AI Engineer at Novartis, you are at the forefront of reimagining medicine through data and artificial intelligence. This role is not just about building models; it is about accelerating drug discovery, optimizing clinical trials, and ultimately improving patient outcomes globally. You will bridge the gap between cutting-edge machine learning research and scalable, enterprise-grade healthcare solutions.

Your work directly impacts how Novartis operates, from streamlining internal workflows to developing predictive models that assist researchers and clinicians. You will tackle complex challenges involving massive, highly regulated datasets, requiring both deep technical expertise and a strong understanding of data privacy and healthcare compliance. The solutions you architect will be deployed at scale, influencing strategic business decisions and patient care pathways.

Expect an environment that is highly collaborative, scientifically rigorous, and deeply mission-driven. You will partner with data scientists, medical researchers, product managers, and software engineers to translate ambiguous business problems into robust AI systems. If you are passionate about leveraging technology to extend and improve people's lives, this role offers unparalleled scale, complexity, and purpose.

2. Common Interview Questions

The questions below represent the typical themes and scenarios you will encounter during your Novartis interviews. While you should not memorize answers, use these to practice structuring your thoughts, especially focusing on how you articulate your past experiences and design decisions.

Resume & Technical Deep Dive

This category focuses heavily on the specifics of your past work. Interviewers want to verify your hands-on experience with APIs, model building, and engineering best practices.

• Walk me through the architecture of the most impactful project on your resume.
• How did you design the API for [Specific Project]? What frameworks did you use and why?
• Explain a time when you had to optimize a machine learning model for better performance or lower latency.
• Describe your process for ensuring code quality and reliability when deploying AI models.
• What were the biggest technical challenges you faced in [Specific Project], and how did you overcome them?

System Design & Workflow

These questions test your ability to architect scalable solutions and think through the entire lifecycle of an AI project.

• How would you design a scalable workflow to process daily batches of clinical trial data and update a predictive model?
• Walk us through your approach to transitioning a model from a local Jupyter notebook to a production environment.
• If we need to serve a model that requires real-time inference with strict latency constraints, how would you architect the serving layer?
• How do you monitor model drift in production, and what automated workflows would you set up to handle it?
• Present a high-level design for a system that ingests unstructured medical texts and securely exposes an NLP model via an API.

Behavioral & Stakeholder Management

Novartis places high value on collaboration. These questions evaluate your emotional intelligence, adaptability, and communication skills.

• Tell me about a time you had to manage conflicting priorities from different stakeholders.
• Describe a situation where you had to explain a highly technical AI concept to a business leader. How did you ensure they understood?
• Can you share an example of a project that failed or did not meet expectations? What did you learn, and how did you communicate this to the team?
• How do you handle situations where a stakeholder has unrealistic expectations about what machine learning can achieve?
• Tell me about a time you successfully collaborated with a cross-functional team to deliver a complex project.

3. Getting Ready for Your Interviews

Preparing for the AI Engineer interview at Novartis requires a balanced approach. While technical competence is non-negotiable, interviewers place an equally strong emphasis on your ability to communicate complex ideas and collaborate with diverse stakeholders. You should approach your preparation by focusing on the following key evaluation criteria:

Technical Execution & Architecture – This assesses your ability to design, build, and deploy machine learning models and APIs. Interviewers at Novartis will evaluate your proficiency in coding, your understanding of model lifecycles, and your ability to architect scalable workflows that integrate seamlessly into existing healthcare platforms. You can demonstrate strength here by clearly explaining the trade-offs in your past technical decisions.

Problem-Solving & Workflow Design – This evaluates how you break down ambiguous, real-world problems. You will be tested on your ability to design end-to-end project workflows, from data ingestion to model deployment. Strong candidates will showcase a structured thought process, anticipating edge cases and operational bottlenecks before they occur.

Stakeholder Management & Communication – Because you will work closely with non-technical teams, including medical researchers and business leaders, this criterion is critical. Interviewers will look for your ability to translate technical jargon into business value, manage expectations, and drive consensus. You must prove you can navigate complex organizational dynamics smoothly.

Culture Fit & Adaptability – Novartis values curiosity, collaboration, and a patient-centric mindset. You will be evaluated on your willingness to learn, your adaptability in a highly regulated environment, and your alignment with the company’s core mission. Prepare to share examples of how you have positively influenced team culture and navigated challenging project pivots.

4. Interview Process Overview

The interview process for an AI Engineer at Novartis is designed to be efficient, respectful of your time, and highly focused on your practical experience. Candidates consistently report a straightforward process that avoids unnecessary technical hurdles, often wrapping up in just three to four comprehensive stages. The hiring team and HR are known to be highly communicative, supportive, and invested in your success throughout the journey.

You will typically begin with an initial HR screening to discuss your background, the role, and the overall hiring timeline. From there, the process shifts into behavioral and technical rounds led by the hiring manager and senior team members. Rather than abstract algorithmic puzzles, expect deep dives into your resume, discussions about your past projects, and practical questions about API development and model deployment. For some teams, the final stage includes a presentation component where you will discuss a project workflow or design with a panel of cross-functional team members.

This visual timeline outlines the typical progression of the Novartis interview process, moving from initial behavioral alignment to deep technical and project-based evaluations. You should use this to pace your preparation, ensuring you are ready to discuss both your technical architecture skills and your stakeholder management experiences by the final rounds. Note that specific stages, like the presentation component, may vary slightly depending on the exact team or location you are interviewing with.

5. Deep Dive into Evaluation Areas

To succeed in the Novartis interview, you must be prepared to discuss your past work with exceptional depth and clarity. The evaluation is heavily indexed on practical application, system design, and behavioral competencies.

Resume & Past Project Deep Dive

Your past experience is the primary lens through which Novartis evaluates your technical capabilities. Interviewers will meticulously review the projects listed on your resume, probing for your specific contributions, the challenges you faced, and the impact of your work. Strong performance means being able to articulate the entire lifecycle of a project, from the initial problem statement to the final deployment and monitoring phases.

Be ready to go over:

• End-to-end model development – Explaining how you gathered data, selected features, trained models, and evaluated performance.
• API design and integration – Discussing how you exposed your models for consumption by other services or front-end applications.
• Overcoming technical roadblocks – Detailing specific instances where a project failed or stalled, and the technical steps you took to resolve the issue.
• Advanced concepts (less common) –
  • Model optimization for low-latency environments.
  • Handling highly imbalanced or noisy datasets (common in healthcare).
  • Versioning and monitoring of deployed machine learning models.

Example questions or scenarios:

• "Walk me through the most complex machine learning pipeline you built in your last role. What were the primary bottlenecks?"
• "Explain how you designed the API for the project listed on your resume. How did you handle authentication and rate limiting?"
• "Tell me about a time your model underperformed in production. How did you diagnose and fix the issue?"

Project Workflow & System Design

Novartis operates at an enterprise scale, meaning your models must be robust, scalable, and maintainable. This area evaluates your ability to design systems that work in the real world, not just in a Jupyter notebook. You may be asked to participate in a project workflow discussion or deliver a presentation detailing how you would architect a specific solution.

Be ready to go over:

• High-level architecture – Mapping out data ingestion, storage, compute, and serving layers.
• MLOps and deployment strategies – Explaining how you handle CI/CD for machine learning, containerization, and orchestration.
• Scalability and reliability – Designing systems that can handle increasing data volumes while maintaining high availability.
• Advanced concepts (less common) –
  • Federated learning approaches for privacy-preserving AI.
  • Designing architectures compliant with healthcare data regulations (e.g., HIPAA, GDPR).

Example questions or scenarios:

• "How would you design an end-to-end workflow to predict patient readmission rates using historical clinical data?"
• "During your presentation, the panel asks: 'How will this architecture scale if our data volume increases tenfold next year?' How do you respond?"
• "Describe your approach to transitioning a model from a research prototype to a production-ready API."

Stakeholder Management & Behavioral Alignment

Building great AI is only half the job; the other half is ensuring it gets adopted. Novartis heavily evaluates your ability to work with non-technical stakeholders, manage expectations, and drive alignment. Strong candidates will demonstrate empathy, clear communication, and a track record of successfully navigating organizational complexity.

Be ready to go over:

• Translating technical concepts – Explaining complex AI mechanisms to business leaders or medical professionals in plain language.
• Handling pushback – Managing situations where stakeholders disagree with your approach or have unrealistic expectations about what AI can achieve.
• Cross-functional collaboration – Highlighting your experience working alongside product managers, data engineers, and domain experts.

Example questions or scenarios:

• "Tell me about a time you had to explain a complex machine learning concept to a non-technical stakeholder who was skeptical of your approach."
• "Describe a situation where a project's requirements kept changing. How did you manage the stakeholder relationships while keeping the project on track?"
• "How do you ensure that the AI solutions you build actually solve the underlying business problem?"

6. Key Responsibilities

As an AI Engineer at Novartis, your day-to-day work revolves around turning complex healthcare data into actionable, AI-driven insights. You will be responsible for designing, training, and optimizing machine learning models that address specific business or scientific challenges. This involves writing production-quality code, building robust data pipelines, and developing APIs that allow other internal systems to seamlessly integrate with your AI solutions.

Beyond coding, a significant portion of your role involves project workflow design and cross-functional collaboration. You will partner closely with domain experts—such as research scientists and clinical trial managers—to understand their needs and translate them into technical requirements. You will actively participate in architectural discussions, ensuring that the solutions you build are scalable, secure, and compliant with rigorous healthcare data standards.

You will also be responsible for the operational lifecycle of your models. This means you will monitor model performance in production, implement MLOps best practices, and continuously iterate on your designs based on new data and stakeholder feedback. Your work will directly bridge the gap between innovative AI research and tangible, enterprise-wide applications that drive the Novartis mission forward.

7. Role Requirements & Qualifications

To be a competitive candidate for the AI Engineer position at Novartis, you must possess a strong blend of software engineering rigor and machine learning expertise, coupled with excellent communication skills.

• Must-have technical skills – Deep proficiency in Python and standard ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn). Strong experience in designing and developing RESTful APIs to serve machine learning models. Solid understanding of software engineering principles, including version control, testing, and CI/CD pipelines.
• Must-have soft skills – Exceptional stakeholder management and the ability to communicate complex technical concepts to non-technical audiences. A collaborative mindset and the ability to thrive in cross-functional teams.
• Experience level – Typically, candidates need 3+ years of industry experience in machine learning engineering, software engineering, or a closely related field, with a proven track record of deploying models to production.
• Nice-to-have skills – Prior experience in the healthcare, pharmaceutical, or life sciences industries. Familiarity with MLOps tools (e.g., MLflow, Kubeflow) and cloud platforms (AWS, Azure, or GCP). Knowledge of data privacy regulations and secure data handling practices.

8. Frequently Asked Questions

Q: How difficult is the AI Engineer interview process at Novartis? Candidates generally rate the difficulty as average. The process is less about tricking you with obscure algorithmic puzzles and more about deeply understanding your practical experience, your ability to build APIs, and how you manage project workflows and stakeholders.

Q: Will there be a live coding or LeetCode-style round? While technical questions are guaranteed, Novartis tends to focus more on resume deep dives, API design, and practical engineering discussions rather than intense, competitive programming-style whiteboard sessions. Expect to discuss how you code and design systems rather than solving abstract puzzles.

Q: What is the presentation component like? For some teams, you will be asked to present a past project or a proposed workflow design to a panel. This is a conversational presentation where different team members will take turns asking questions. It is designed to test your communication skills, your ability to defend your technical choices, and how well you handle Q&A.

Q: How important is healthcare domain knowledge for this role? While it is considered a strong "nice-to-have," it is rarely a strict requirement unless specified in the job description. However, demonstrating an interest in the healthcare domain and an understanding of the constraints (like data privacy and compliance) will make you a much stronger candidate.

Q: How long does the interview process typically take? The process is generally described as short and efficient, often consisting of just three main stages (HR, Hiring Manager, and a Technical/Panel round). You can typically expect the entire process to wrap up within a few weeks, with HR remaining highly communicative throughout.

9. Other General Tips

• Master Your Resume: The most common technical questions will come directly from your resume. You must be able to explain every technology, design choice, and outcome listed. If you claim experience with an API framework or ML tool, be prepared to discuss its internal workings and trade-offs.
• Structure Your Behavioral Answers: Use the STAR method (Situation, Task, Action, Result) for all behavioral and stakeholder management questions. Novartis interviewers want to hear specific, quantifiable results and clearly understand your individual contribution to the team's success.
• Focus on the "Why" in System Design: When discussing project workflows or architecture, do not just list the tools you would use. Explain why you chose them over alternatives, focusing on scalability, maintainability, and business impact.

• Prepare for the Panel Q&A: If your interview includes a presentation round, practice delivering it to peers beforehand. Anticipate where the panel might interrupt with questions, and practice maintaining your composure and answering thoughtfully without getting defensive.
• Show Passion for the Mission: Novartis is deeply mission-driven. Take time to research their recent AI initiatives or drug discovery pipelines. Weaving this knowledge into your answers shows genuine interest and sets you apart from candidates treating it as just another tech job.

10. Summary & Next Steps

Interviewing for the AI Engineer role at Novartis is a unique opportunity to showcase your ability to blend cutting-edge technical skills with profound real-world impact. The hiring team is looking for practical problem solvers—engineers who can not only train robust models but also build the APIs and workflows necessary to deploy them at an enterprise scale.

To succeed, focus your preparation on mastering the narrative of your past projects. Be ready to dive deep into your technical architecture decisions while simultaneously proving that you can manage complex stakeholder relationships with empathy and clarity. Remember that the process is designed to be straightforward and collaborative; the interviewers want to see how you would perform as a trusted colleague on their team.

This compensation data provides a baseline expectation for the AI Engineer role. Keep in mind that actual offers will vary based on your specific location, your level of seniority, and the specialized technical skills you bring to the table. Use this information to anchor your expectations and inform your negotiations when the time comes.

Approach your upcoming interviews with confidence. You have the technical foundation required; now it is about communicating your value clearly and demonstrating your alignment with the Novartis mission. For further insights, question breakdowns, and peer experiences, continue exploring the resources available on Dataford. You are well-equipped to excel in this process—good luck!