Agentic Applications Expert, Rapid Prototyping

📍 Job Overview

Job Title: Agentic Applications Expert, Rapid Prototyping Company: Novartis Location: Hyderabad, India Job Type: Full-Time Category: Technology / Data Science / Biomedical Research Operations Date Posted: 2026-05-08 Experience Level: Mid-Senior Level (5-10 years) Remote Status: On-site

🚀 Role Summary

• Spearhead the development and deployment of cutting-edge Agentic Systems within Biomedical Research (BR) to accelerate drug discovery and development cycles.
• Drive innovation by integrating advanced AI/ML methodologies, including generative AI and large language models (LLMs), into R&D processes.
• Foster a culture of AI expertise across BR by collaborating with internal teams and external partners to enhance drug discovery efficiency and outcomes.
• Contribute to Novartis's digital transformation strategy by positioning AI-aided drug discovery as a key differentiator in the pharmaceutical industry.

📝 Enhancement Note: This role sits at the intersection of advanced AI/ML, software development, and pharmaceutical R&D operations. The "Agentic Applications Expert" title, combined with "Rapid Prototyping," indicates a hands-on role focused on quickly building, testing, and deploying AI-driven solutions for complex scientific problems. The emphasis on "Agentic Systems," LLM fine-tuning, and orchestration frameworks points towards a highly specialized and in-demand skill set within the AI and operations space.

📈 Primary Responsibilities

• Design, build, implement, and deploy a portfolio of Agentic applications in collaboration with BR drug discovery project teams and existing drug discovery platforms to deliver tangible improvements.
• Elevate the level of Agentic expertise and capabilities across the Biomedical Research organization through knowledge sharing, training, and hands-on support.
• Contribute to the strategic positioning of AI-aided drug discovery, enabling novel therapeutic discoveries, shortening development timelines, and increasing operational efficiency within BR.
• Regularly communicate, engage, and align with the Artificial Intelligence and Computational Sciences (AICS) teams, the broader BR data science community, and senior scientific stakeholders to ensure cohesive strategy and execution.
• Initiate and lead high-value internal collaborations across BR to identify, recommend, and evaluate opportunities for Agentic system integration and partnership.
• Provide subject matter expertise, input, and evangelism for the field of AI across Biomedical Research, acting as a champion for advanced computational approaches.

📝 Enhancement Note: The responsibilities highlight a dual focus on technical execution (building and deploying applications) and strategic influence (raising expertise, evangelism, collaboration). The emphasis on "drug discovery project teams" and "drug discovery platforms" suggests that operational efficiency will be measured by its direct impact on the R&D pipeline.

🎓 Skills & Qualifications

Education:

• A strong foundation in a quantitative field such as Computer Science, Data Science, Computational Biology, Bioinformatics, Applied Mathematics, or a related discipline. While a specific degree is not listed, these fields are typically required for advanced AI/ML roles.

Experience:

• 4+ years of significant experience in the innovation, development, deployment, and continuous support of Machine Learning (ML) tools and models, including proven experience in an enterprise environment.
• 2+ years of hands-on experience in fine-tuning and prompting large language models (LLMs), including techniques such as Supervised Fine-Tuning (SFT), LoRA, Reinforcement Learning from Human Feedback (RLHF), Online Policy Optimization (ORPO), and Gradient-based Policy Optimization (GRPO). Experience with pre-training and post-training LLMs is a plus.
• 2+ years of hands-on experience and a strong understanding of Retrieval-Augmented Generation (RAG) techniques, tool-use agents (e.g., ReAct style systems), and orchestration frameworks (e.g., LangChain, LangGraph, CrewAI).

Required Skills:

• Proficiency in Python programming language, essential for developing and deploying AI/ML models and applications.
• Strong understanding and practical experience using version control systems for software development, such as GitHub, Git, Subversion, or Bitbucket.
• Proficiency with automated testing methodologies, code review processes, and infrastructure-as-code (IaC) tools like Terraform or CloudFormation for managing cloud resources.
• Demonstrated ability to work with emerging technologies, coupled with pragmatic insight into their integration into business solutions to meet stakeholder requirements.
• Entrepreneurial spirit with a proactive, can-do attitude and a deep passion for understanding and applying new technologies.

Preferred Skills:

• Expertise in translating advanced analytics insights into actionable strategies for a large Research organization.
• Deep understanding of the drug development lifecycle and the pharmaceutical R&D landscape.
• A track record of innovation demonstrated through publications, patents, or open-source contributions related to agent frameworks or computational drug discovery.

📝 Enhancement Note: The experience requirements are highly specific and indicate a need for a candidate with deep, practical expertise in modern AI/ML, particularly LLMs and agent-based systems. The "Nice to have" section emphasizes the value of domain knowledge in drug development, suggesting that candidates with this background will have a significant advantage.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate successful development and deployment of ML models or AI applications within an enterprise setting, showcasing the end-to-end lifecycle from ideation to production support.
• Provide case studies detailing the implementation of LLMs, including fine-tuning efforts (SFT, LoRA, etc.) and prompt engineering strategies, with clear articulation of the problem statement and solution.
• Showcase practical experience with RAG architectures, agentic systems, and orchestration frameworks (LangChain, LangGraph, CrewAI), including examples of how these were used to solve complex problems.
• Present examples of infrastructure-as-code implementations (e.g., Terraform scripts) and robust version-controlled codebases (e.g., GitHub repositories) that highlight best practices in software development and deployment.

Process Documentation:

• Detailed documentation of ML/AI project workflows, including data preprocessing, model training, evaluation, and deployment phases.
• Examples of how automated testing, code reviews, and CI/CD pipelines were integrated into the development process to ensure software quality and reliability.
• Documentation illustrating the application of agentic system design principles, including agent orchestration, tool integration, and response generation mechanisms.
• Evidence of process optimization efforts within AI/ML projects, such as improving model performance, reducing inference latency, or enhancing system scalability.

📝 Enhancement Note: For a "Rapid Prototyping" role, the portfolio should emphasize speed of execution without sacrificing quality. Applicants should be prepared to discuss not just the final solution, but the iterative process, the challenges encountered, and how they quickly adapted to deliver functional prototypes.

💵 Compensation & Benefits

Salary Range:

• Based on industry benchmarks for similar roles in Hyderabad, India, for a candidate with 5-10 years of experience in advanced AI/ML and software development, the estimated annual salary range would be between ₹1,800,000 and ₹3,500,000 INR. This range accounts for the specialized skills in LLMs, agentic systems, and rapid prototyping, as well as the enterprise-level experience required.

Benefits:

• Comprehensive health insurance coverage for employees and dependents.
• Retirement savings plans (e.g., Provident Fund, Gratuity) in line with Indian labor laws and company policy.
• Paid time off, including vacation days, sick leave, and public holidays.
• Opportunities for professional development, including access to training, conferences, and certifications in AI/ML and related fields.
• Potential for performance-based bonuses and stock options, reflecting contributions to company goals.
• Employee assistance programs offering confidential counseling and support services.
• Access to company-provided resources and technology for efficient work.

Working Hours:

• Standard full-time working hours, typically 40 hours per week, with potential for flexibility to accommodate project deadlines and rapid prototyping needs. Adherence to Indian labor laws regarding working hours and overtime is expected.

📝 Enhancement Note: Salary estimation is based on market data for senior AI/ML engineers in Hyderabad, India, considering the specific demands of LLM fine-tuning, agent frameworks, and enterprise deployment. Benefits are aligned with typical offerings for multinational corporations in India, with a focus on professional development relevant to advanced technology roles.

🎯 Team & Company Context

🏢 Company Culture

Industry: Pharmaceutical and Biotechnology. Novartis is a global leader focused on reimagining medicine to improve and extend people's lives. The company operates in a highly regulated and research-intensive environment. Company Size: Large Enterprise (Novartis globally employs over 100,000 people). This scale offers significant resources, a broad impact, and complex operational structures. Founded: 1996 (through the merger of Ciba-Geigy and Sandoz). The company has a long history of innovation in healthcare.

Team Structure:

• This role is within the Artificial Intelligence and Computational Sciences (AICS) team, which leads Biomedical Research's (BR) exploration and application of advanced AI/ML. The AICS team partners with various drug discovery teams and BR's data science community.
• The reporting structure likely involves a lead data scientist or AICS manager, with close collaboration across BR's scientific leadership and IT/digital transformation teams.
• Cross-functional collaboration is a cornerstone, involving close partnerships with drug discovery project teams, platform engineers, and potentially external academic and industry AI leaders.

Methodology:

• Data-driven decision-making is paramount, leveraging internal and external R&D data.
• Agile methodologies are implied by the "Rapid Prototyping" aspect, focusing on iterative development and quick deployment of AI/ML solutions.
• A strong emphasis on scientific rigor, ethical AI deployment, and patient-centric outcomes guides operational processes.

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

📝 Enhancement Note: Novartis's commitment to digital transformation and AI integration suggests a culture that values innovation, scientific advancement, and collaboration. The size of the company means operations roles often involve navigating complex organizational structures and influencing stakeholders across different departments.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as an expert or senior specialist within the AI/ML and R&D operations domain. It requires significant hands-on technical expertise combined with the ability to influence and drive adoption of new technologies. Reporting Structure: The role will report to a manager or lead within the AICS team, with extensive interaction with senior scientists and project leads across Biomedical Research. This structure allows for both technical mentorship and strategic project involvement. Operations Impact: The impact of this role is directly tied to accelerating drug discovery timelines, identifying novel therapeutic targets, and improving the efficiency and success rates of R&D programs. Success in this role translates to tangible improvements in bringing life-saving medicines to patients faster.

Growth Opportunities:

• Specialization: Deepen expertise in specific areas of agentic systems, LLMs, or computational drug discovery, becoming a go-to subject matter expert for the organization.
• Leadership: Transition into team leadership roles within AICS or similar AI/Data Science functions, managing projects and mentoring junior team members.
• Cross-Functional Mobility: Move into broader R&D strategy roles, product management for AI tools, or roles focused on scaling AI solutions across different therapeutic areas or business units within Novartis.
• Continuous Learning: Access to cutting-edge research, industry conferences, and internal training programs to stay at the forefront of AI and drug discovery advancements.

📝 Enhancement Note: The "Expert" title and emphasis on rapid prototyping suggest a role that is both technically challenging and offers significant growth potential for individuals passionate about applying AI to solve complex scientific problems. The career path likely involves increasing scope of influence and strategic input.

🌐 Work Environment

Office Type: This is an on-site role in Hyderabad, India, indicating a traditional office-based work environment. Novartis typically provides modern office facilities designed for collaboration and productivity. Office Location(s): Hyderabad, Telangana, India. This location is a major hub for pharmaceutical R&D and technology in India, offering access to a skilled talent pool and a vibrant ecosystem.

Workspace Context:

• The workspace will likely be collaborative, encouraging interaction with fellow data scientists, AI experts, and drug discovery researchers.
• Access to high-performance computing resources, cloud platforms, and relevant software tools will be provided to facilitate rapid prototyping and model development.
• Opportunities for direct engagement with R&D teams will foster a deep understanding of scientific challenges and enable effective application of AI solutions.

Work Schedule:

• The standard work schedule will be full-time, aligned with Indian business hours. However, the "Rapid Prototyping" nature of the role may necessitate occasional flexible hours or extended work periods to meet critical project milestones or respond to urgent research needs.

📝 Enhancement Note: While on-site, the emphasis on rapid prototyping suggests a dynamic and potentially fast-paced work environment where collaboration and access to robust technical infrastructure are key enablers.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A review of your resume and application to assess alignment with the required technical skills and experience, particularly in LLMs, agentic systems, and Python.
• Technical Interview(s): In-depth discussions focusing on your experience with LLM fine-tuning (SFT, LoRA, RLHF), RAG, agent orchestration frameworks (LangChain, LangGraph, CrewAI), Python development, and version control. Expect coding challenges or system design questions.
• Portfolio Review/Presentation: A session where you will present your past projects, showcasing your ability to build and deploy AI solutions, particularly in a rapid prototyping context. Focus on demonstrating impact and technical depth.
• Behavioral/Cultural Fit Interview: Assessment of your entrepreneurial spirit, problem-solving approach, collaboration style, and alignment with Novartis's values and commitment to diversity and inclusion.
• Hiring Manager Interview: Final discussion to assess overall fit, strategic thinking, and alignment with the team's goals.

Portfolio Review Tips:

• Focus on Impact: For each project, clearly articulate the problem you solved, the solution you developed, the technologies used (especially LLMs, RAG, agents), and the quantifiable results or impact achieved (e.g., time saved, efficiency gained, insights generated).
• Demonstrate Rapid Prototyping: Highlight projects where you quickly iterated and delivered functional prototypes. Explain your methodology for rapid development and validation.
• Showcase Code Quality: Be prepared to walk through code snippets, demonstrating proficiency in Python, use of libraries like LangChain, and adherence to best practices (e.g., version control, testing).
• Explain Technical Choices: Be ready to justify your technology choices, especially regarding LLM fine-tuning techniques, RAG implementation, and agent orchestration strategies.
• Tailor to Novartis: If possible, draw parallels between your past work and the challenges faced in drug discovery or pharmaceutical R&D to demonstrate domain relevance.

Challenge Preparation:

• LLM/Agentic System Design: Prepare to design a system that uses LLMs and agents to solve a hypothetical drug discovery problem (e.g., hypothesis generation, literature review automation, experimental design).
• Coding Exercises: Practice Python coding problems, potentially involving data manipulation, API interactions, or basic ML model implementation.
• RAG/Tool-Use Scenarios: Be ready to discuss how you would implement RAG for a specific knowledge base or design an agent that can utilize specific tools (e.g., databases, simulation software).

📝 Enhancement Note: The interview process emphasizes practical, hands-on skills and the ability to translate complex AI concepts into real-world applications. The portfolio is critical for demonstrating this capability, especially the rapid prototyping aspect.

🛠 Tools & Technology Stack

Primary Tools:

• LLM Fine-Tuning & Prompting: Experience with frameworks and techniques for fine-tuning LLMs (SFT, LoRA, RLHF, ORPO, GRPO) and advanced prompt engineering.
• Agent Orchestration Frameworks: Hands-on expertise with LangChain, LangGraph, CrewAI, or similar platforms for building complex AI agents and multi-agent systems.
• Retrieval-Augmented Generation (RAG): Proficiency in implementing RAG pipelines for knowledge retrieval and enhancing LLM responses.
• Python: Advanced proficiency in Python is a core requirement, including its scientific computing libraries.

Analytics & Reporting:

• While not explicitly listed, expect to work with data science and analytics tools for model evaluation and performance monitoring. This could include libraries like Pandas, NumPy, Scikit-learn, and potentially visualization tools.

CRM & Automation:

• Not directly applicable to this R&D-focused role, but understanding how AI applications integrate with broader enterprise systems might be beneficial.

Infrastructure & Development:

• Version Control: GitHub, Git, Subversion, Bitbucket.
• Infrastructure-as-Code (IaC): Terraform, CloudFormation for managing cloud infrastructure.
• Cloud Platforms: Familiarity with major cloud providers (AWS, Azure, GCP) is highly probable, given the nature of AI/ML development and deployment.

📝 Enhancement Note: The technology stack is heavily focused on modern AI/ML development, particularly LLMs and agent-based systems. Proficiency in Python and IaC tools is crucial for building and deploying these applications within an enterprise environment.

👥 Team Culture & Values

Operations Values:

• Innovation & Pioneering: A commitment to exploring and adopting cutting-edge AI technologies to drive scientific breakthroughs.
• Collaboration & Partnership: Emphasis on working effectively with diverse teams across BR and AICS to achieve shared goals.
• Patient Focus: All efforts are ultimately directed towards improving patient outcomes and accelerating the delivery of new therapies.
• Agility & Speed: A culture that values rapid prototyping and efficient iteration to quickly deliver value.
• Data-Driven Excellence: Reliance on robust data analysis and scientific rigor to guide development and decision-making.

Collaboration Style:

• Cross-Functional Integration: Expect to work closely with bench scientists, computational biologists, data scientists, and IT professionals, requiring strong communication skills to bridge technical and scientific domains.
• Proactive Engagement: The role encourages initiating collaborations and actively seeking opportunities to apply AI solutions.
• Knowledge Sharing: A culture that promotes sharing insights, best practices, and lessons learned within the AICS team and the broader BR data science community.

📝 Enhancement Note: Novartis's culture emphasizes scientific advancement within a structured corporate environment. The AICS team likely fosters a collaborative and innovative atmosphere, balancing the rapid pace of AI development with the rigorous demands of pharmaceutical R&D.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving AI Landscape: Staying current with the fast pace of advancements in LLMs, agentic systems, and AI methodologies requires continuous learning.
• Integration Complexity: Integrating new AI applications into existing, complex drug discovery workflows and platforms can be challenging.
• Data Availability and Quality: Ensuring access to high-quality, relevant data for training and fine-tuning AI models can be a significant hurdle in pharmaceutical R&D.
• Translating Research to Production: Moving from rapid prototypes to robust, scalable, and production-ready AI systems requires careful planning and execution.

Learning & Development Opportunities:

• Cutting-Edge Research: Exposure to and participation in the development of state-of-the-art AI models and agentic frameworks.
• Industry Conferences & Certifications: Opportunities to attend leading AI and computational biology conferences and pursue relevant certifications.
• Mentorship: Access to senior AI experts and computational scientists within Novartis for guidance and skill development.
• Cross-Disciplinary Projects: Engaging in diverse projects that span different therapeutic areas and R&D stages, broadening your understanding of drug discovery.

📝 Enhancement Note: This role offers the chance to work on challenging, impactful problems at the forefront of AI and drug discovery. The company's investment in digital transformation suggests a strong commitment to employee development in these critical areas.

💡 Interview Preparation

Strategy Questions:

• "Describe a situation where you had to rapidly prototype an AI solution to solve a complex problem. What was your approach, and what were the key takeaways?" (Focus on iterative development, speed, and learning).
• "How would you approach building an agentic system to assist drug discovery scientists in identifying novel therapeutic targets? What LLM techniques and orchestration frameworks would you consider and why?" (Demonstrate understanding of LLMs, RAG, agents, and their application in R&D).
• "Walk me through your experience fine-tuning a large language model. What challenges did you face, and how did you overcome them? What metrics did you use to evaluate success?" (Highlight specific techniques like SFT, LoRA, RLHF and your problem-solving skills).

Company & Culture Questions:

• "What interests you about Novartis's digital transformation strategy and its application in drug discovery?" (Show research into Novartis's AI initiatives and your alignment with their goals).
• "How do you stay updated with the rapidly evolving field of AI and agentic systems?" (Emphasize continuous learning and passion for emerging technologies).
• "Describe a time you had to collaborate with non-technical stakeholders (e.g., scientists) to implement a technical solution. How did you ensure alignment and understanding?" (Focus on communication and bridging technical/scientific gaps).

Portfolio Presentation Strategy:

• Structure for Impact: Begin with a concise overview of the project's objective and your role. Clearly detail the problem, your proposed AI solution, the technical implementation (highlighting LLM, RAG, agent specifics), and the tangible results or impact.
• Emphasize Rapid Prototyping: For relevant projects, specifically call out the speed of development, iterative process, and how you quickly validated concepts.
• Showcase Technical Depth: Be prepared to dive into the specifics of your code, model architecture, fine-tuning parameters, and orchestration logic. Use GitHub repositories or code snippets effectively.
• Quantify Results: Wherever possible, use metrics to demonstrate the value of your work (e.g., % reduction in cycle time, accuracy improvements, number of hypotheses generated, efficiency gains).
• Connect to Novartis: If possible, frame your experiences in the context of pharmaceutical R&D challenges to show relevance.

📝 Enhancement Note: Interview preparation should focus on demonstrating practical expertise in modern AI/ML, particularly LLMs and agent systems, and the ability to apply these skills in a fast-paced, R&D-focused environment. The portfolio is your primary tool for showcasing these capabilities.

📌 Application Steps

To apply for this Agentic Applications Expert position:

• Submit your application through the official Novartis Careers portal link provided.
• Curate Your Portfolio: Select 2-3 key projects that best showcase your experience with LLMs (fine-tuning, prompting), agentic systems (LangChain, CrewAI), RAG, and rapid prototyping. Ensure each project clearly outlines the problem, solution, technology stack, and quantifiable impact.
• Optimize Your Resume: Tailor your resume to highlight keywords and skills mentioned in the job description, such as "Agentic Systems," "Large Language Models," "LLM fine-tuning," "RAG," "LangChain," "Python," "Terraform," and "Drug Discovery." Quantify achievements wherever possible.
• Prepare Your Presentation: Practice presenting your portfolio projects, focusing on clear, concise explanations of technical details and impactful results. Be ready to discuss your rapid prototyping methodology and lessons learned.
• Research Novartis: Familiarize yourself with Novartis's digital transformation initiatives, their focus on AI in drug discovery, and their commitment to innovation. Understand how your skills can contribute to their strategic goals.

⚠️ 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.