Senior Expert (Rapid Prototyping)

📍 Job Overview

Job Title: Senior Expert (Rapid Prototyping)

Company: Novartis

Location: Hyderabad, India

Job Type: Full-Time

Category: Technology & Innovation (AI/ML focus within Life Sciences)

Date Posted: June 23, 2026

Experience Level: 5-10 years

Remote Status: Hybrid

🚀 Role Summary

• Drive innovation in drug discovery by designing and deploying advanced generative AI and agentic systems.

• Develop rapid prototypes and scalable applications leveraging foundation models, retrieval systems, and workflow orchestration.

• Build production-ready large language model (LLM) applications with robust context handling, tool integration, and comprehensive monitoring.

• Translate emerging AI capabilities into practical, reusable solutions that empower researchers and accelerate decision-making.

• Uphold modern software engineering best practices, including rigorous testing, version control, and lifecycle management, within an enterprise AI context.

📝 Enhancement Note: The role title "Senior Expert (Rapid Prototyping)" combined with the description's focus on transforming drug discovery through generative AI and agentic systems strongly indicates a position within a dedicated AI/ML innovation or R&D team, likely serving a GTM (Go-To-Market) enablement or scientific research acceleration function. The emphasis on rapid prototyping suggests a fast-paced, iterative development environment focused on proving concepts and quickly delivering value to internal research teams.

📈 Primary Responsibilities

• Design, develop, and deploy generative artificial intelligence (AI) solutions tailored to support drug discovery and biomedical research workflows.

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

• Build and maintain production-ready large language model (LLM) applications, ensuring robust context management, seamless tool integration, and diligent performance monitoring.

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

• Implement and adhere to engineering best practices, including comprehensive testing strategies, effective benchmarking, meticulous version control, and robust lifecycle management for AI systems.

• Foster strong collaboration with cross-functional teams, including AI researchers, data scientists, and domain experts, to deliver AI solutions that align with critical research priorities.

• Identify and champion opportunities to enhance scientist productivity through the implementation of AI assistants and intelligent workflow automation.

• Contribute significantly to the architecture, scalability, security, and overall reliability of AI-powered systems deployed within the organization.

• Actively share best practices, develop reusable components, and provide technical guidance to diverse teams working on AI applications across the organization.

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

📝 Enhancement Note: The responsibilities highlight a blend of deep technical AI/ML development and strategic application within a scientific context. The emphasis on "rapid prototyping" and "translating emerging capabilities" indicates a role focused on innovation and bridging the gap between cutting-edge research and practical application, rather than solely maintaining existing systems. This implies a need for strong problem-solving skills and the ability to quickly iterate on solutions.

🎓 Skills & Qualifications

Education:

• While not explicitly stated, a Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Computational Biology, or a related quantitative field is typically expected for a Senior Expert role of this nature. A Ph.D. in a relevant field would be a strong asset. Experience:

• 5+ years of hands-on experience building and deploying artificial intelligence (AI) or machine learning (ML) systems in production environments.

• Minimum of 2+ years of direct experience specifically designing and implementing large language model (LLM) applications, including proficiency in retrieval augmented generation (RAG) and tool integration techniques. Required Skills:

• Strong proficiency in Python, including extensive experience developing scalable services, robust application programming interfaces (APIs), and reusable software libraries.

• Proven ability to deliver end-to-end AI solutions, encompassing prompt design, rigorous evaluation methodologies, and effective performance monitoring.

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

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

• Solid understanding of evaluation metrics relevant to generative AI systems, including considerations for quality, reliability, latency, and cost-effectiveness.

• Awareness of critical enterprise considerations such as security, data governance, and responsible AI practices in the deployment of AI solutions. Preferred Skills:

• Prior experience in drug discovery, computational biology, bioinformatics, or other related biomedical research environments.

• A strong track record of contributions through publications, patents, or active participation in open-source projects within the AI or computational sciences domains.

📝 Enhancement Note: The requirements emphasize production-level AI/ML experience, particularly with LLMs and RAG, which are critical for building sophisticated AI applications in research. The inclusion of software engineering best practices and enterprise considerations suggests a need for candidates who can not only develop novel AI but also ensure its reliability, scalability, and responsible deployment within a regulated industry like pharmaceuticals.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase at least 2-3 significant projects demonstrating the successful design, development, and deployment of AI/ML systems, with a strong emphasis on generative AI or LLM applications.

• For each project, clearly articulate the problem statement, the AI/ML approach taken, the specific tools and technologies utilized (e.g., Python, LangChain, cloud platforms), and the impact achieved.

• Provide evidence of rapid prototyping capabilities, highlighting projects where concepts were quickly developed and validated.

• Include examples of scalable applications or services built, demonstrating an understanding of production deployment and lifecycle management.

• Quantify the impact of your work, using metrics related to efficiency gains, research acceleration, cost savings, or improved decision-making for researchers. Process Documentation:

• For portfolio projects, clearly document the end-to-end AI development lifecycle: from initial problem definition and data exploration to model training/fine-tuning, prompt engineering, system integration, testing, and deployment.

• Illustrate workflows for integrating LLMs with existing research tools or databases, detailing the RAG implementation or other context-enrichment strategies.

• Demonstrate how you applied software engineering principles (version control, testing, CI/CD) to ensure the reliability and maintainability of AI solutions.

• Include examples of how you evaluated and monitored AI model performance in a production setting, along with strategies for continuous improvement.

📝 Enhancement Note: For a role focused on rapid prototyping and deploying advanced AI in a research environment, a portfolio demonstrating practical application of LLMs, RAG, and workflow orchestration is crucial. Candidates should be prepared to walk through the technical architecture and development process of their showcased projects, highlighting their ability to translate complex AI concepts into tangible solutions.

💵 Compensation & Benefits

Salary Range:

• Based on the Senior Expert title, 5-10 years of experience, and the location in Hyderabad, India, a competitive salary range would be estimated between ₹20,00,000 to ₹35,00,000 per annum. This estimate considers the specialized nature of AI/ML expertise, the demand for such skills in the Indian tech market, and the typical compensation bands for senior technical roles in multinational corporations. Benefits:

• Comprehensive health insurance coverage for employees and dependents.

• Paid time off, including annual leave, sick leave, and public holidays.

• Retirement savings plan (e.g., Provident Fund contributions).

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

• Relocation support for candidates moving to Hyderabad.

• Access to company-provided resources and cutting-edge technology for AI development.

• Potential for performance-based bonuses or incentives.

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

• Standard full-time working hours are typically 40 hours per week. Given the "Hybrid" work arrangement and the nature of rapid prototyping, there may be flexibility, but a commitment to project deadlines and collaborative sprints is expected.

📝 Enhancement Note: The salary estimate is based on market research for senior AI/ML roles in Hyderabad, India, considering industry benchmarks and the specialized skills required. Novartis, as a large multinational pharmaceutical company, is expected to offer a comprehensive benefits package.

🎯 Team & Company Context

🏢 Company Culture

Industry: Pharmaceutical & Biotechnology. Novartis operates at the intersection of advanced technology and life sciences, focusing on developing innovative medicines to improve and extend people's lives. This industry demands rigorous scientific standards, ethical practices, and a commitment to patient well-being.

Company Size: Large Enterprise (Novartis is a global company with over 80,000 employees worldwide). This size implies access to significant resources, established processes, and opportunities for broad impact, but also requires navigating larger organizational structures.

Founded: 1996 (through the merger of Ciba-Geigy and Sandoz). Novartis has a long heritage in the pharmaceutical industry, combining decades of experience with a forward-looking approach to innovation, particularly in areas like AI.

Team Structure:

• The role is likely part of a dedicated AI/ML innovation team or a central Digital/AI R&D division focused on accelerating scientific discovery. This team would likely comprise AI/ML scientists, data engineers, software engineers, and domain experts (e.g., computational biologists).

• Reporting structure will likely be to an AI/ML Lead, Head of Innovation, or a Director within R&D Digital.

• Cross-functional collaboration is paramount, involving close partnerships with research