Senior Expert (Rapid Prototyping)

📍 Job Overview

Job Title: Senior Expert (Rapid Prototyping)

Company: Novartis

Location: Hyderabad, India

Job Type: Full time

Category: Technology / Science & Research Operations

Date Posted: 2026-08-07

Experience Level: 5-10 years

Remote Status: Hybrid

🚀 Role Summary

• Spearhead the design and deployment of advanced generative artificial intelligence (GenAI) solutions to accelerate drug discovery and biomedical research.

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

• Build production-ready Large Language Model (LLM) applications with advanced context handling, tool integration, and robust monitoring capabilities.

• Translate cutting-edge GenAI and agentic system advancements into practical, reusable solutions for scientific teams.

• Drive the adoption of AI assistants and workflow automation to significantly enhance scientist productivity and research efficiency.

📝 Enhancement Note: This role sits at the critical intersection of AI/ML engineering and scientific research operations. The "Rapid Prototyping" aspect implies a focus on agile development, quick iteration, and demonstrating the feasibility of novel AI applications within a complex R&D environment. The emphasis on "agentic systems" suggests a forward-thinking approach to AI, moving beyond simple generative tasks to more autonomous research assistance.

📈 Primary Responsibilities

• Design, develop, and deploy generative AI solutions specifically tailored to support drug discovery and biomedical research workflows, ensuring alignment with scientific objectives.

• Construct rapid prototypes and scalable applications that effectively combine foundation models, retrieval-augmented generation (RAG) techniques, and robust workflow orchestration.

• Engineer production-grade LLM applications featuring sophisticated context management, seamless tool integration, and comprehensive performance monitoring.

• Proactively translate emerging generative AI and agentic system paradigms into practical, reusable, and impactful solutions for the research community.

• Implement and uphold modern software engineering best practices, including rigorous testing, performance benchmarking, efficient version control, and diligent lifecycle management.

• Foster strong collaboration with cross-functional teams, including data scientists, researchers, and IT professionals, to deliver AI solutions that directly address research priorities.

• Identify and champion opportunities to boost scientist productivity through the strategic implementation of AI assistants and automated workflows.

• Contribute to the architectural design, scalability planning, security hardening, and overall reliability of AI-powered systems within the enterprise.

• Actively share best practices, reusable code components, and technical guidance across diverse teams engaged in AI application development.

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

📝 Enhancement Note: The responsibilities highlight a full-stack AI/ML engineering role with a strong emphasis on practical application and deployment. The "rapid prototyping" title suggests a need for speed and agility, while the mention of "production-ready" applications and "scalable services" indicates that the prototypes must be designed with eventual production in mind. Collaboration with researchers is key, implying a need for strong communication and domain understanding.

🎓 Skills & Qualifications

Education: While not explicitly stated, a Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Computational Biology, or a related quantitative field is highly advantageous for a Senior Expert role. A strong Bachelor's degree with extensive relevant experience may also be considered.

Experience: Minimum of 5 years of professional experience in building and deploying AI/ML systems in production environments. A dedicated 2+ years of hands-on experience specifically in designing and implementing LLM applications, including Retrieval Augmented Generation (RAG) and tool integration, is essential.

Required Skills:

• Proven track record of 5+ years in building and deploying AI/ML systems in production environments.

• Minimum 2 years of hands-on experience designing and implementing Large Language Model (LLM) applications, including Retrieval Augmented Generation (RAG) and tool integration.

• Strong proficiency in Python, with demonstrated experience in developing scalable services, robust APIs, and reusable libraries.

• Experience in delivering end-to-end AI solutions, encompassing prompt design, rigorous evaluation, and effective monitoring strategies.

• Familiarity with generative AI frameworks such as LangChain, LangGraph, or similar ecosystems for building sophisticated AI applications.

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

• Knowledge of evaluation metrics for generative AI systems, focusing on aspects like quality, reliability, and cost-effectiveness.

• Awareness of security, data governance, and responsible AI principles within enterprise-level environments. Preferred Skills:

• Prior experience in drug discovery, computational biology, or other biomedical research domains.

• A history of publications, patents, or significant open-source contributions in AI, ML, or computational sciences.

• Experience with cloud platforms (e.g., AWS, Azure, GCP) for AI/ML deployments.

• Familiarity with containerization technologies like Docker and orchestration tools like Kubernetes.

• Understanding of data engineering principles and building data pipelines for AI/ML.

📝 Enhancement Note: The requirements clearly indicate a need for a hybrid profile: strong AI/ML engineering skills combined with an understanding of scientific research contexts. The emphasis on Python and specific frameworks like LangChain/LangGraph points towards a practical, hands-on development role. The "Senior Expert" title suggests that candidates should be able to operate with a high degree of autonomy and provide technical leadership.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrable examples of end-to-end AI/ML system deployments, showcasing the full lifecycle from concept to production.

• Case studies detailing the design and implementation of LLM applications, specifically highlighting RAG, tool integration, and prompt engineering strategies.

• Projects showcasing the development of scalable services, APIs, and reusable Python libraries for AI/ML applications.

• Evidence of applying modern software engineering practices, including robust testing methodologies, version control workflows, and CI/CD implementation.

• Examples of evaluating and monitoring AI systems, with clear metrics and insights into performance and reliability improvements. Process Documentation:

• Workflow designs for AI-powered research processes, illustrating how AI models and systems integrate into existing scientific workflows.

• Documentation of prototype development processes, emphasizing agile methodologies, rapid iteration, and feedback incorporation.

• Implementation guides for AI solutions, detailing deployment procedures, configuration, and integration with other research systems.

• Performance analysis reports for AI systems, showcasing metrics related to accuracy, efficiency, cost, and user satisfaction.

📝 Enhancement Note: For a "Senior Expert (Rapid Prototyping)" role, a portfolio is crucial. It should not just list projects but demonstrate a clear understanding of the process of building and deploying AI solutions. Candidates should be prepared to articulate the problem statement, their technical approach, the challenges encountered, the solutions implemented, and the measurable impact or potential impact of their work, especially in the context of accelerating research.

💵 Compensation & Benefits

Salary Range: Based on the experience level (5-10 years), the Senior Expert title, and the location in Hyderabad, India, a competitive salary range can be estimated. For a highly specialized role in AI/ML with a focus on Generative AI and LLMs, the annual salary could range from ₹2,000,000 to ₹4,000,000 (approximately $24,000 - $48,000 USD, depending on current exchange rates). This range accounts for the significant demand for AI expertise and the specific technical requirements.

Benefits:

• Comprehensive health insurance coverage (medical, dental, vision) for employees and dependents.

• Generous paid time off (PTO), including vacation days, sick leave, and public holidays.

• Retirement savings plan or provident fund contributions.

• Opportunities for professional development, including training, certifications, and conference attendance.

• Relocation support provided for candidates moving to Hyderabad.

• Potential for performance-based bonuses and stock options/grants.

• Access to cutting-edge technology and research facilities.

• Employee assistance programs (EAP) for well-being support.

Working Hours: The standard working hours are typically 40 hours per week, aligning with full-time employment. While the role is described as hybrid, specific on-site days and flexibility will likely be determined by team needs and project demands, requiring a balance between collaborative work and focused individual effort.

📝 Enhancement Note: Salary estimates are based on industry benchmarks for senior AI/ML engineers in major Indian tech hubs, adjusted for the specialized nature of GenAI/LLM expertise and the company's standing as a global pharmaceutical leader. Benefits are typical for large multinational corporations in the life sciences sector, with specific emphasis on professional growth and relocation assistance as indicated in the job posting.

🎯 Team & Company Context

🏢 Company Culture

Industry: Pharmaceutical / Biotechnology. Novartis operates at the forefront of the healthcare industry, dedicated to reimagining medicine to improve and extend people's lives. This sector demands rigorous scientific inquiry, data integrity, and a commitment to patient outcomes.

Company Size: Novartis is a large multinational corporation, typically employing tens of thousands of individuals globally. This scale offers significant resources, opportunities for cross-functional collaboration, and access to a vast network of expertise. For operations professionals, this means working within established frameworks while having the potential to influence processes across large teams.

Founded: Novartis was formed in 1996 through the merger of Ciba-Geigy and Sandoz. With a long history rooted in innovation, the company has a strong legacy of scientific advancement and a forward-looking approach to addressing global health challenges.

Team Structure:

• The AI/ML team, likely within a broader R&D or Digital Transformation division, will comprise specialists in various AI domains, including GenAI, LLMs, ML engineering, and potentially data science and computational biology.

• Reporting structure is expected to be hierarchical, with the Senior Expert likely reporting to an AI/ML Lead, Manager, or Director, and potentially mentoring junior engineers.

• Cross-functional collaboration will be extensive, involving close partnerships with bench scientists, computational biologists, IT infrastructure teams, and project managers to ensure AI solutions are relevant and effectively integrated into research pipelines. Methodology:

• Data Analysis & Insights: Emphasizes data-driven decision-making, leveraging advanced analytics and AI models to uncover insights from complex biological and chemical data.

• Workflow Planning & Optimization: Focuses on streamlining research processes, automating repetitive tasks, and designing efficient workflows that accelerate the pace of discovery.

• Automation & Efficiency: Drives the adoption of AI and machine learning to automate experimental design, data analysis, literature review, and other research-related activities, thereby increasing operational efficiency.

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

📝 Enhancement Note: Novartis's industry and size suggest a culture that values scientific rigor, innovation, and ethical practices. The specific role of "Senior Expert (Rapid Prototyping)" within this context implies a need for individuals who can bridge the gap between cutting-edge AI research and practical application in a highly regulated and complex scientific environment. The emphasis on accelerating therapies points to a results-oriented and patient-centric culture.

📈 Career & Growth Analysis

Operations Career Level: This role represents a senior individual contributor position focused on specialized technical expertise in AI/ML engineering, specifically within the domain of rapid prototyping for generative AI and LLMs. It sits at a level where significant technical autonomy is expected, alongside the ability to influence technical direction and mentor junior team members. It's a key role in operationalizing advanced AI capabilities within the R&D lifecycle.

Reporting Structure: The Senior Expert will likely report to a Team Lead, Manager, or Director of AI/ML or Digital R&D. This structure provides guidance and strategic alignment while allowing the expert significant freedom in technical execution and problem-solving. Collaboration will extend across research teams, IT, and potentially other operational functions.

Operations Impact: The primary impact of this role is accelerating the drug discovery and development process. By rapidly prototyping and deploying AI solutions, the Senior Expert will enable researchers to analyze data more effectively, identify potential drug candidates faster, and optimize experimental designs, directly contributing to bringing life-saving therapies to patients more quickly. This role is pivotal in enhancing the operational efficiency and innovative capacity of the R&D organization.

Growth Opportunities:

• Technical Specialization: Deepen expertise in specific areas of GenAI, LLMs, agentic systems, or related fields, potentially becoming a recognized subject matter expert within Novartis.

• Leadership Development: Transition into roles with direct people management responsibilities, leading AI/ML teams, or taking on technical program management for AI initiatives.

• Cross-functional Mobility: Move into roles within different R&D departments or digital transformation initiatives, applying AI expertise to new scientific challenges or operational areas.

• Innovation Leadership: Contribute to strategic AI roadmap development, identify new research avenues, and drive the adoption of transformative AI technologies across the organization.

📝 Enhancement Note: The "Senior Expert" title signifies a high level of technical mastery and potential for leadership. Growth opportunities will likely focus on deepening specialized AI/ML knowledge, moving into management, or taking on broader strategic responsibilities within the company's innovation landscape. The emphasis on rapid prototyping suggests a career path for those who thrive on innovation and quick execution.

🌐 Work Environment

Office Type: This position is designated as Hybrid. It involves a blend of remote work and on-site presence at the Hyderabad office. This setup aims to balance the flexibility of remote work with the benefits of in-person collaboration, team building, and access to specialized on-site resources.

Office Location(s): The primary office location is Hyderabad, India. This location is a major hub for technology and pharmaceutical research in India, offering a dynamic environment with access to talent and infrastructure.

Workspace Context:

• Collaborative Environment: The hybrid model encourages collaboration through scheduled team meetings, brainstorming sessions, and project-specific discussions, both virtually and in person. Proximity to other AI/ML experts and researchers in the Hyderabad office will foster knowledge sharing.

• Operations Tools & Technology: Employees will have access to Novartis's robust IT infrastructure, including high-performance computing resources, cloud environments, and specialized software for AI/ML development and research.

• Team Interaction: Regular interaction with a diverse team of AI specialists, data scientists, and domain experts will be a key aspect of the work environment, facilitating learning and problem-solving.

Work Schedule: Standard full-time working hours (approx. 40 hours/week) are expected. The hybrid nature allows for some flexibility in structuring the work week, with specific on-site requirements likely determined by project needs, team synchronization, and collaborative activities. This flexibility is crucial for accommodating the iterative nature of rapid prototyping and deep analytical work.

📝 Enhancement Note: The hybrid model is standard for many tech-centric roles in large organizations, offering a balance. The Hyderabad location suggests access to a good talent pool and resources. The focus on AI/ML means the workspace will be equipped with advanced computing and software necessary for demanding development tasks.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A review of your resume and application, focusing on alignment with the required skills and experience, particularly in AI/ML, LLMs, and Python.

• Technical Interview(s): In-depth discussions covering your experience with AI/ML system design, LLM application development (RAG, tool integration), Python programming, software engineering best practices, and familiarity with AI frameworks. Expect coding challenges or system design questions.

• Portfolio Presentation: A session where you present selected projects from your portfolio, detailing the problem, your approach, technical challenges, solutions, and outcomes. This is where you demonstrate your rapid prototyping capabilities and impact.

• Behavioral & Cultural Fit Interview: Assessment of your collaboration style, problem-solving approach, learning agility, and alignment with Novartis's values (e.g., integrity, innovation, patient focus).

• Final Interview: May involve discussions with senior leadership to assess strategic thinking and overall fit for the Senior Expert role.

Portfolio Review Tips:

• Showcase End-to-End Projects: Highlight projects that demonstrate the full lifecycle of AI/ML development, from ideation and prototyping to deployment and monitoring.

• Focus on Impact: Quantify the impact of your work whenever possible. For this role, emphasize how your prototypes accelerated research, improved efficiency, or enabled new scientific discoveries.

• Detail Your Process: Clearly articulate the problem statement, your technical approach, the specific tools and frameworks used (especially LangChain, LangGraph, Python), challenges faced, and how you overcame them.

• Demonstrate Rapid Prototyping: Include examples where you quickly iterated on ideas, built functional prototypes, and gathered feedback to refine solutions.

• Technical Depth: Be prepared to discuss architectural decisions, trade-offs, scaling considerations, and best practices in software engineering and AI development.

Challenge Preparation:

• System Design: Practice designing scalable AI systems, considering components like data ingestion, model training/fine-tuning, inference serving, monitoring, and user interfaces.

• Coding Exercises: Brush up on Python, data structures, algorithms, and potentially specific libraries relevant to AI/ML (e.g., NumPy, Pandas, potentially TensorFlow/PyTorch if applicable).

• LLM Application Scenarios: Prepare to discuss how you would build an LLM application for a specific research task (e.g., summarizing research papers, generating hypotheses, querying complex datasets).

• Problem-Solving Scenarios: Be ready to tackle hypothetical problems related to AI deployment in a research setting, focusing on efficiency, accuracy, and ethical considerations.

📝 Enhancement Note: The interview process is designed to thoroughly assess both technical expertise and practical application skills. A strong portfolio demonstrating hands-on experience with LLMs, GenAI frameworks, and a structured approach to rapid prototyping will be critical for success. Candidates should prepare to articulate not just what they built, but how and why, with a focus on impact and efficiency.

🛠 Tools & Technology Stack

Primary Tools:

• Python: The core programming language for AI/ML development, data analysis, and building services. Proficiency is a must.

• Generative AI Frameworks: LangChain, LangGraph, or similar libraries for orchestrating LLM interactions, managing prompts, integrating tools, and building complex agentic workflows.

• Machine Learning Libraries: Libraries such as NumPy, Pandas, Scikit-learn for data manipulation, analysis, and traditional ML tasks. Familiarity with deep learning frameworks like TensorFlow or PyTorch may also be beneficial.

• Version Control: Git is essential for collaborative development, code management, and tracking changes.

Analytics & Reporting:

• Data Visualization Tools: Tools like Matplotlib, Seaborn, Plotly, or potentially BI platforms (e.g., Tableau, Power BI, if integrated into research workflows) for analyzing model performance and research data.

• Monitoring Tools: Systems for tracking application performance, model drift, error rates, and resource utilization (e.g., Prometheus, Grafana, or cloud-native monitoring services).

• Benchmarking Tools: Frameworks or custom scripts for evaluating the performance, accuracy, and efficiency of AI models and applications.

CRM & Automation:

• While not a direct CRM role, understanding how AI solutions integrate with research data management systems or project tracking tools is relevant.

• Workflow Orchestration Tools: Beyond GenAI frameworks, experience with broader workflow management tools (e.g., Airflow, Prefect) could be advantageous for scaling applications.

• API Development: Experience with frameworks like Flask or FastAPI for building and deploying AI models as services.

📝 Enhancement Note: The technology stack emphasizes modern AI/ML development practices. Proficiency in Python and specific GenAI frameworks like LangChain is paramount. The role requires not just model development but also the engineering skills to deploy and manage these models as scalable services, including robust monitoring and evaluation.

👥 Team Culture & Values

Operations Values:

• Innovation: A strong drive to explore and implement novel AI technologies to push the boundaries of scientific discovery.

• Integrity: Upholding the highest ethical standards in data handling, AI development, and research practices, ensuring responsible AI deployment.

• Collaboration: A commitment to working effectively across diverse teams, sharing knowledge, and fostering a supportive environment for collective problem-solving.

• Patient Focus: A deep understanding that the ultimate goal of the work is to improve patient lives by accelerating the development of new therapies.

• Agility: Embracing a mindset of rapid iteration, continuous learning, and adaptability to quickly respond to evolving research needs and technological advancements.

Collaboration Style:

• Cross-functional Integration: Actively engaging with researchers, computational biologists, data scientists, and IT professionals to ensure AI solutions are practical, relevant, and well-integrated into existing research ecosystems.

• Process Review & Feedback: Participating in regular code reviews, design discussions, and project retrospectives to continuously improve processes, systems, and team performance.

• Knowledge Sharing: Proactively sharing insights, best practices, reusable code, and lessons learned through documentation, presentations, and informal discussions to uplift the entire team's capabilities.

📝 Enhancement Note: Novartis, as a leading pharmaceutical company, will likely foster a culture that blends scientific rigor with a drive for innovation and a strong ethical compass. For an AI/ML role focused on rapid prototyping, a culture that encourages experimentation, learning from failures, and fast iteration will be crucial. Collaboration and a patient-centric mindset are foundational.

⚡ Challenges & Growth Opportunities

Challenges:

• Translating Research Needs to AI Solutions: Bridging the gap between complex, often ill-defined scientific research questions and actionable AI/ML problem statements.

• Rapid Iteration vs. Robustness: Balancing the need for quick prototyping and experimentation with the requirement for reliable, scalable, and production-ready solutions in a regulated environment.

• Data Complexity & Availability: Working with diverse, potentially sparse, and complex biological/biomedical datasets, and addressing challenges related to data quality, annotation, and access.

• Keeping Pace with AI Advancements: The AI field evolves rapidly; staying current with the latest models, frameworks, and techniques while applying them effectively to specific research problems.

• Integration into Existing Workflows: Ensuring new AI tools and prototypes seamlessly integrate with established research processes and IT infrastructure without causing disruption.

Learning & Development Opportunities:

• Advanced AI/ML Specialization: Opportunities to deepen expertise in areas like LLMs, agentic AI, reinforcement learning, or specific applications in computational biology and drug discovery.

• Industry Conferences & Certifications: Support for attending leading AI/ML conferences (e.g., NeurIPS, ICML) and pursuing relevant certifications to enhance skills and knowledge.

• Mentorship & Leadership: Access to mentorship from senior leaders within Novartis's AI/ML and R&D organizations, with pathways for developing leadership and strategic planning capabilities.

• Exposure to Cutting-Edge Research: Direct involvement in projects at the forefront of AI application in life sciences, providing unique learning experiences and contributing to impactful discoveries.

📝 Enhancement Note: The challenges are typical for advanced AI roles in scientific industries, requiring strong technical skills, adaptability, and excellent problem-solving abilities. The growth opportunities are substantial, offering clear paths for technical mastery, leadership, and significant impact within a leading pharmaceutical company.

💡 Interview Preparation

Strategy Questions:

• Operations Strategy: "How would you approach designing a rapid prototyping framework for generative AI in drug discovery, considering both speed and potential for production deployment?" (Preparation: Focus on agile methodologies, modular design, clear MVP definitions, and feedback loops.)

• Collaboration & Stakeholder Management: "Describe a time you had to explain a complex AI concept to non-technical stakeholders (e.g., researchers). How did you ensure understanding and buy-in for your prototype?" (Preparation: Use the STAR method, emphasizing clear communication, analogies, and focusing on the business/research value.)

• Problem-Solving: "Imagine a researcher needs an AI assistant to help them sift through thousands of research papers for specific molecular interactions. How would you prototype such a system using LLMs and RAG?" (Preparation: Outline your approach step-by-step, including data sources, LLM choice, prompt engineering, RAG implementation, and evaluation metrics.)

Company & Culture Questions:

• Company Operations Culture: "What do you understand about Novartis's commitment to innovation and patient impact? How would your role as a Senior Expert in Rapid Prototyping contribute to these goals?" (Preparation: Research Novartis's mission, recent innovations, and values. Connect your skills to their strategic objectives.)

• Operations Team Dynamics: "How do you envision collaborating with researchers and other AI experts within Novartis? What is your preferred team dynamic for rapid prototyping?" (Preparation: Emphasize your collaborative approach, willingness to share knowledge, and ability to work effectively in cross-functional, potentially fast-paced teams.)

• Operations Impact Measurement: "How would you measure the success and impact of a rapid prototype you develop? What metrics would you track?" (Preparation: Discuss metrics related to development speed, user feedback, scientific relevance, potential for future scalability, and ultimately, contribution to research acceleration.)

Portfolio Presentation Strategy:

• Storytelling: Structure your portfolio presentations as compelling narratives. Start with the problem/opportunity, detail your innovative solution and technical process, highlight challenges and your problem-solving approach, and conclude with the results, impact, and lessons learned.

• Metrics & ROI: Clearly present any quantitative results or demonstrable impact of your prototypes. For R&D, this might be time saved, efficiency gained, or potential for accelerated discovery. Quantify where possible.

• Interactive Demonstration: If possible, include live demos or well-produced video walkthroughs of your prototypes to showcase functionality and user experience.

• Company Alignment: Tailor your presentation to highlight projects most relevant to Novartis's industry (pharma, biotech) and strategic goals (drug discovery acceleration, AI innovation).

📝 Enhancement Note: Interview preparation should focus on demonstrating a blend of deep technical expertise in AI/ML and LLMs, practical software engineering skills, and a strong understanding of how to apply these to real-world scientific challenges. The ability to articulate your process, showcase impact, and align with Novartis's mission will be key differentiators.

📌 Application Steps

To apply for this operations position:

• Submit your application through the Novartis Careers portal link provided.

• Portfolio Customization: Curate your portfolio to prominently feature projects demonstrating rapid prototyping of GenAI/LLM applications, Python development, RAG implementation, and software engineering best practices. Prioritize projects with measurable impact on efficiency or acceleration.

• Resume Optimization: Ensure your resume clearly highlights your 5+ years of AI/ML experience, 2+ years with LLMs/RAG, and strong Python skills. Use keywords from the job description and quantify your achievements where possible.

• Interview Preparation: Practice articulating your technical approach, problem-solving methodologies, and project outcomes. Prepare to present your portfolio effectively, focusing on the "how" and "why" behind your solutions and their impact.

• Company Research: Thoroughly research Novartis's mission, current AI initiatives, and its role in pharmaceutical innovation. Understand their commitment to patient impact and how your expertise aligns with their strategic objectives.

⚠️ 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.