Senior Expert (Rapid Prototyping)

📍 Job Overview

Job Title: Senior Expert (Rapid Prototyping)

Company: Novartis

Location: Dublin, Ireland

Job Type: FULL_TIME

Category: Revenue Operations (with a focus on AI/ML for R&D acceleration)

Date Posted: June 23, 2026

Experience Level: 5-10 years

Remote Status: Hybrid

🚀 Role Summary

• Lead the design and deployment of advanced generative artificial intelligence (AI) and agentic systems to accelerate drug discovery and biomedical research.

• Develop rapid prototypes and scalable applications that integrate foundation models, retrieval systems, and sophisticated workflow orchestration.

• Create production-ready large language model (LLM) applications with advanced context handling, seamless tool integration, and robust monitoring capabilities.

• Translate emerging AI and agentic approaches into practical, reusable solutions that empower scientific researchers and enhance decision-making.

• Uphold rigorous engineering best practices, including comprehensive testing, performance benchmarking, version control, and lifecycle management for AI solutions.

📝 Enhancement Note: While the role is in R&D, the operational aspects of deploying and scaling AI solutions, managing workflows, and ensuring system reliability align it with Revenue Operations principles, particularly in enabling faster go-to-market for new discoveries. The focus on "rapid prototyping" and "scalable applications" indicates a strong need for operations professionals who can bridge the gap between experimental AI and production-ready systems.

📈 Primary Responsibilities

• Design and deploy generative AI solutions that directly support critical drug discovery and biomedical research workflows, focusing on efficiency and speed.

• Build rapid prototypes and scalable applications by combining cutting-edge foundation models, advanced retrieval systems, and efficient workflow orchestration tools.

• Develop production-ready large language model (LLM) applications, ensuring robust context handling, seamless tool integration, and comprehensive monitoring systems are in place.

• Proactively translate emerging generative AI and agentic system approaches into practical, reusable, and impactful solutions for research teams.

• Apply and enforce modern software engineering best practices, including rigorous testing, performance benchmarking, meticulous version control, and effective lifecycle management for all AI deployments.

• Collaborate effectively with cross-functional teams, including researchers, data scientists, and IT, to deliver AI solutions that align precisely with strategic research priorities.

• Identify and implement opportunities to significantly enhance scientist productivity through the use of AI assistants and intelligent workflow automation.

• Contribute to the architectural design, scalability, security, and overall reliability of AI-powered systems within the research environment.

• Actively share best practices, reusable code components, and technical guidance across various teams developing AI applications to foster a collaborative and efficient ecosystem.

• Continuously evaluate emerging tools, frameworks, and technologies to advance the company's generative AI capabilities and maintain a competitive edge.

📝 Enhancement Note: The responsibilities emphasize a blend of technical AI development and operational deployment, crucial for a "Senior Expert" role in this domain. The focus on "scalable applications," "production-ready," "testing," "version control," and "lifecycle management" are core operational competencies. The mention of "enhancing scientist productivity" and "workflow automation" directly points to operational efficiency gains.

🎓 Skills & Qualifications

Education:

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field. Advanced degrees are often preferred for senior roles involving cutting-edge research and development. Experience:

• 5+ years of hands-on experience building and deploying AI or Machine Learning (ML) systems in production environments, demonstrating a track record of successful implementation and maintenance.

• Minimum of 2 years of dedicated experience in designing and developing large language model (LLM) applications, including practical expertise with Retrieval Augmented Generation (RAG) techniques and effective tool integration strategies. Required Skills:

• Strong proficiency in Python programming, with a proven ability to develop scalable services, robust application programming interfaces (APIs), and reusable libraries for AI applications.

• Demonstrated experience in delivering end-to-end AI solutions, encompassing meticulous prompt design, rigorous evaluation methodologies, and comprehensive system monitoring.

• Familiarity with generative AI frameworks such as LangChain, LangGraph, or similar ecosystems, showcasing an understanding of modern LLM development patterns.

• Proven experience applying modern software engineering practices, including comprehensive testing, code reviews, version control (e.g., Git), and continuous integration/continuous deployment (CI/CD) pipelines.

• Solid understanding of evaluation metrics for generative AI systems, covering aspects like output quality, system reliability, and cost-efficiency considerations.

• Awareness of critical enterprise considerations including security, data governance, and responsible AI practices, ensuring ethical and compliant AI development. Preferred Skills:

• Experience in drug discovery, computational biology, or biomedical research environments, providing domain-specific context for AI applications.

• A history of publications, patents, or open-source contributions in artificial intelligence or computational sciences, indicating leadership and innovation in the field.

• Experience with cloud platforms (AWS, Azure, GCP) for deploying and managing AI/ML workloads.

• Familiarity with containerization technologies like Docker and Kubernetes for scalable deployments.

📝 Enhancement Note: The required experience levels (5+ years in AI/ML, 2+ years in LLM/RAG) suggest a need for candidates with proven operational deployment experience, not just theoretical knowledge. The emphasis on Python, API development, testing, and CI/CD are hallmarks of operations-focused technical roles.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase a portfolio demonstrating the successful design, development, and deployment of AI/ML systems into production environments.

• Include case studies highlighting the creation of rapid prototypes and scalable applications, particularly those involving foundation models and workflow orchestration.

• Present examples of end-to-end LLM application development, emphasizing context handling, tool integration, and monitoring strategies.

• Provide evidence of applying software engineering best practices, such as version control, testing frameworks, and CI/CD pipelines, within AI projects.

• Demonstrate experience in translating emerging AI concepts into practical, operational solutions with clear business or research impact. Process Documentation:

• Document the entire lifecycle of AI/ML projects managed, from initial concept and feasibility through to production deployment and ongoing maintenance.

• Detail the methodologies used for prompt engineering, model evaluation, and performance monitoring in production settings.

• Outline the processes for integrating AI solutions with existing research workflows and systems, ensuring seamless adoption and maximum efficiency.

• Showcase strategies for managing dependencies, versioning models and code, and ensuring the scalability and reliability of deployed AI services.

📝 Enhancement Note: For a "Senior Expert" role focused on rapid prototyping and deployment, a strong portfolio is paramount. It needs to showcase not just the AI models themselves, but the operational rigor, engineering practices, and the ability to scale solutions from prototype to production. This directly relates to process optimization and system implementation.

💵 Compensation & Benefits

Salary Range: €63,490.00 - €117,910.00 EUR Annual.

Benefits:

• Comprehensive Insurance Plans

• Robust Retirement Plans

• Dedicated Wellbeing Resources

• Global Recognition Programs

• Flexible and Hybrid Working Options

• Minimum of 14 weeks paid parental leave

Working Hours: 40 hours per week (standard full-time, with potential for hybrid flexibility).

📝 Enhancement Note: The salary range provided is typical for a senior technical role in Dublin, Ireland, reflecting the cost of living and the specialized skills required. The benefits package is standard for a large multinational corporation like Novartis, emphasizing employee well-being and work-life balance, which are attractive to operations professionals seeking stable and supportive employment.

🎯 Team & Company Context

🏢 Company Culture

Industry: Pharmaceutical & Biotechnology (Healthcare). Novartis is a global leader in reimagining medicine, focusing on innovative therapies and scientific breakthroughs. This industry context means a strong emphasis on data integrity, regulatory compliance, and impactful outcomes for patient health.

Company Size: Large multinational corporation (implied by global presence and extensive benefits). For operations professionals, this typically means established processes, opportunities for specialization, and interaction with diverse, global teams.

Founded: Novartis was formed in 1996 through the merger of Ciba-Geigy and Sandoz. This history indicates a blend of established pharmaceutical expertise with a forward-looking approach to innovation.

Team Structure:

• The role likely sits within a dedicated AI/ML team or an advanced research technology group, potentially part of R&D or a central innovation hub.

• Reporting structure will likely be to an AI/ML Lead or Head of Research Technology, with potential dotted lines to project stakeholders in research departments.

• Cross-functional collaboration is critical, involving close partnerships with bench scientists, computational biologists, data scientists, IT infrastructure teams, and potentially legal/compliance for ethical AI deployment. Methodology:

• Data-driven approach to problem-solving, utilizing scientific rigor and advanced analytics.

• Emphasis on rapid iteration and experimentation, especially in the prototyping phase, balanced with robust engineering for production systems.

• Collaborative development and knowledge sharing, fostering an environment where best practices are disseminated and adopted.

• Focus on translating complex scientific challenges into actionable AI solutions.

Company Website: https://www.novartis.com/

📝 Enhancement Note: Understanding Novartis's position as a major pharmaceutical player is key. Operations roles here are not just about internal efficiency but about contributing to the acceleration of life-saving drug development. The "rapid prototyping" aspect suggests a culture that embraces innovation and fast-paced development within a structured, research-intensive environment.

📈 Career & Growth Analysis

Operations Career Level: This is a "Senior Expert" role, indicating a high level of technical expertise and experience. It suggests a position that contributes significantly to strategy, mentors junior team members, and independently drives complex projects. In an operations context, this means leading initiatives for AI system implementation, optimization, and scaling within the R&D function.

Reporting Structure: The role reports to a lead or manager within an AI/ML or advanced technology group. This structure allows for focused technical guidance while still requiring collaboration across various research and development departments. The "expert" title implies a degree of autonomy in technical decision-making.

Operations Impact: The primary impact of this role is accelerating the drug discovery and development pipeline. By designing and deploying AI solutions, this role directly contributes to faster identification of potential therapies, improved research efficiency, and ultimately, the speed at which transformative medicines reach patients. This is a high-impact position within the life sciences sector.

Growth Opportunities:

• Specialization: Deepen expertise in specific areas of generative AI, LLMs, or agentic systems, potentially becoming a go-to subject matter expert.

• Leadership: Transition into team lead or management roles, overseeing AI/ML projects and teams, or guiding the strategic direction of AI adoption in R&D.

• Cross-functional Mobility: Move into roles that focus on broader AI strategy, data governance, or technology integration across different business units within Novartis.

• Industry Recognition: Contribute to publications, patents, or open-source projects, enhancing professional reputation and potentially leading to external speaking opportunities.

📝 Enhancement Note: The "Senior Expert" title implies a career path beyond individual contribution, with potential for technical leadership or management. The impact is directly tied to the company's core mission of drug development, making it a highly rewarding area for operations professionals interested in science and innovation.

🌐 Work Environment

Office Type: Hybrid. This indicates a blend of remote work flexibility and in-office collaboration, typical for modern tech-focused roles within large corporations.

Office Location(s): Dublin, Ireland (NOCC - likely Novartis Oncology Capability Center or similar). This specific location suggests a hub for advanced technology and specialized research functions within Novartis.

Workspace Context:

• The work environment will likely be collaborative, with opportunities to engage with a diverse group of scientists, engineers, and AI specialists.

• Access to cutting-edge computing resources and AI/ML platforms will be essential for rapid prototyping and development.

• Expect a dynamic setting where scientific curiosity meets technological innovation, fostering a culture of continuous learning and problem-solving.

• The hybrid model allows for focused individual work at home and collaborative sessions, brainstorming, and team alignment in the office.

Work Schedule: Standard full-time working hours (approximately 40 hours/week), with the flexibility offered by the hybrid arrangement. This allows for structured workdays while accommodating personal needs, crucial for deep concentration required in AI development and operations.

📝 Enhancement Note: The hybrid nature of the role is a key aspect for work-life balance. The Dublin location suggests being part of a significant operational hub for Novartis, offering exposure to a large and dynamic team environment.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Recruiter or HR call to assess basic qualifications, experience, and alignment with company culture.

• Technical Interview(s): Series of interviews with hiring managers and senior team members. Expect questions on AI/ML fundamentals, Python proficiency, LLM architecture, RAG implementation, software engineering practices, and problem-solving scenarios.

• Case Study/Prototyping Exercise: A practical challenge to assess your ability to design, prototype, or analyze an AI solution. This could involve prompt engineering, system design, or evaluating an existing approach. Preparation should focus on demonstrating your thought process and operational considerations.

• Cross-functional/Behavioral Interview: Discussion focused on collaboration, communication, stakeholder management, and how you handle challenges within a team environment.

• Final Interview: Often with a senior leader, focusing on strategic alignment, leadership potential, and overall fit within the organization.

Portfolio Review Tips:

• Curate Selectively: Showcase 3-5 of your strongest projects that best represent the required skills (AI/ML production systems, LLM/RAG applications, Python development, software engineering practices).

• Focus on Impact: For each project, clearly articulate the problem statement, your role, the technical approach, the operational challenges overcome, and the measurable outcomes or impact (e.g., efficiency gains, speed improvements, cost reductions). Use specific metrics.

• Highlight Process: Detail the software engineering practices you applied (testing, version control, CI/CD), the prototyping methodology, and how you ensured scalability and reliability.

• Demonstrate Problem-Solving: Be prepared to walk through your thought process, especially for challenging aspects of the project, and how you arrived at your solutions.

• Tailor to Novartis: If possible, subtly align your project examples with the pharmaceutical or biomedical research context, demonstrating an understanding of the company's domain.

Challenge Preparation:

• System Design: Practice designing scalable AI systems, considering data flow, model deployment, monitoring, and potential failure points.

• Code Review: Be ready to analyze and critique Python code snippets, identifying potential bugs, performance issues, or adherence to best practices.

• Prompt Engineering: Prepare to discuss strategies for effective prompt design for various LLM tasks, including few-shot learning and prompt chaining.

• Troubleshooting: Anticipate scenarios where an AI system might fail in production and outline your systematic approach to diagnosing and resolving the issue.

📝 Enhancement Note: The emphasis on a portfolio and practical challenges indicates that Novartis values demonstrated ability over theoretical knowledge. Preparing concrete examples of operationalizing AI solutions will be key.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Language: Python (essential, with strong proficiency expected).

• AI/ML Frameworks: PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers.

• Generative AI/LLM Ecosystems: LangChain, LangGraph, LlamaIndex, or similar libraries for building LLM applications.

• Version Control: Git (GitHub, GitLab, Bitbucket).

• Containerization: Docker, Kubernetes (for scalable deployments).

Analytics & Reporting:

• Data Visualization: Matplotlib, Seaborn, Plotly.

• Dashboarding Tools: Tableau, Power BI, or internal BI solutions for monitoring AI system performance and research impact.

• Experiment Tracking: MLflow, Weights & Biases, or similar tools for managing AI experiments.

CRM & Automation:

• While not directly CRM-focused, understanding how AI solutions can integrate with research data management systems or lab information management systems (LIMS) is crucial.

• Workflow Orchestration: Airflow, Prefect, or internal workflow management tools to automate AI pipelines.

• Cloud Platforms: Experience with cloud services like AWS, Azure, or GCP for AI/ML workloads (e.g., SageMaker, Azure ML, Google AI Platform).

📝 Enhancement Note: This role requires a strong foundation in Python and ML libraries, with specific expertise in the emerging LLM/GenAI ecosystem. Experience with deployment and operational tools like Docker, Kubernetes, and cloud platforms is vital for bringing prototypes to production.

👥 Team Culture & Values

Operations Values:

• Innovation: A drive to explore and implement novel AI solutions that push the boundaries of drug discovery.

• Collaboration: Working effectively across diverse teams, sharing knowledge, and building solutions together.

• Excellence: A commitment to high-quality engineering, rigorous testing, and reliable system performance.

• Impact: A focus on delivering tangible results that accelerate scientific progress and benefit patients.

• Responsibility: Adherence to ethical AI principles, data governance, and security best practices.

Collaboration Style:

• Cross-functional Integration: Seamlessly working with research scientists, data scientists, and IT to ensure AI solutions meet research needs and are technically sound.

• Proactive Communication: Regularly updating stakeholders on progress, challenges, and potential solutions, ensuring transparency and alignment.

• Knowledge Sharing: Actively participating in internal forums, code reviews, and documentation to disseminate learnings and best practices related to AI development and deployment.

📝 Enhancement Note: Novartis's culture will likely reflect a balance between scientific rigor and innovative ambition. For operations professionals, this means being adaptable, data-driven, and focused on enabling scientific breakthroughs through technology.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapid Technological Evolution: Keeping pace with the extremely fast-moving field of generative AI and LLMs, and discerning which technologies are truly impactful and scalable.

• Bridging Research and Production: Translating experimental AI models and prototypes into robust, scalable, and reliable production systems that meet enterprise standards.

• Data Integration and Quality: Working with complex, diverse, and potentially sensitive biological and chemical data, ensuring its quality and effective use in AI models.

• Ethical AI and Governance: Navigating the complexities of responsible AI development, data privacy, and regulatory considerations within the pharmaceutical industry.

Learning & Development Opportunities:

• Specialized AI/ML Training: Access to internal and external training programs focused on advanced AI techniques, LLMs, and agentic systems.

• Industry Conferences: Opportunities to attend leading AI and pharmaceutical technology conferences to stay abreast of the latest trends and network with peers.

• Mentorship: Potential to be mentored by leading experts within Novartis or to mentor junior team members, fostering leadership skills.

• Exposure to Cutting-Edge Research: Direct involvement in projects that are at the forefront of drug discovery, providing unique learning experiences.

📝 Enhancement Note: The challenges in this role are significant but come with substantial opportunities for professional growth, particularly in a rapidly evolving field like generative AI within a highly impactful industry.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you successfully deployed a complex AI/ML system into a production environment. What were the biggest operational challenges, and how did you overcome them?" (Focus on your process, testing, monitoring, and problem-solving).

• "How would you approach building a rapid prototype for an AI assistant to help drug discovery scientists query experimental data efficiently?" (Detail your technology choices, workflow design, and prototyping methodology).

• "Discuss your experience with Retrieval Augmented Generation (RAG). What are the key considerations for implementing an effective RAG system in a research context, and how do you evaluate its performance?" (Emphasize practical implementation and evaluation metrics). Company & Culture Questions:

• "Novartis is focused on reimagining medicine. How do you see generative AI contributing to this mission, and what is your role in that journey?" (Connect your technical skills to the company's overarching goals).

• "How do you ensure collaboration and effective communication when working with cross-functional teams, especially those with different technical backgrounds (e.g., bench scientists)?" (Highlight your communication and stakeholder management skills).

• "What are your thoughts on responsible AI and data governance in the pharmaceutical industry, and how would you incorporate these principles into your work?" (Showcase your awareness of ethical and compliance considerations). Portfolio Presentation Strategy:

• Storytelling: Structure your portfolio presentations around compelling narratives: the problem, your solution, the process, and the impact.

• Visuals: Use diagrams, charts, and concise code snippets to illustrate complex concepts, system architectures, and performance metrics.

• Quantify Impact: Whenever possible, use numbers and data to demonstrate the value of your work (e.g., "reduced query time by 50%", "improved model accuracy by 15%").

• Highlight Operational Rigor: Explicitly mention the software engineering practices, testing methodologies, and deployment strategies you employed.

• Engage and Discuss: Be prepared to answer detailed questions about your projects, defend your technical decisions, and discuss alternative approaches.

📝 Enhancement Note: Interview preparation should focus on demonstrating not just AI knowledge, but also operational expertise in deploying, scaling, and managing AI systems, with a clear understanding of the drug discovery domain.

📌 Application Steps

To apply for this Senior Expert (Rapid Prototyping) position at Novartis:

• Submit your application through the Novartis careers portal linked.

• Tailor Your Resume: Highlight keywords and experiences directly relevant to AI/ML production systems, LLMs, RAG, Python development, and software engineering best practices. Quantify achievements with specific metrics.

• Curate Your Portfolio: Select 3-5 of your most impactful projects that showcase your ability to design, build, and deploy AI solutions. Focus on case studies demonstrating rapid prototyping and production readiness.

• Prepare Your Narrative: Practice articulating your project details, your role, the challenges you faced, and the operational impact of your work, especially for the portfolio review and interview stages.

• Research Novartis: Understand their mission, recent innovations, and how AI/ML is integrated into their R&D processes. This will help tailor your responses and demonstrate genuine interest.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.