Job Description Summary

This position is pivotal in identifying and incubating cutting-edge AI technologies that align with the strategic goals of the company, enhancing the capabilities in data-driven decision-making and is crucial in defining and promoting best practices in AI model development and deployment.
AI Engineer through their forward-thinking ensure seamless integration of innovative AI solutions into existing frameworks, ensuring they are scalable, reliable, and tailored to meet the unique demands of the pharmaceutical industry. The AI Engineer will con-tribute to our mission of advancing healthcare through technology, ultimately improving patient outcomes and driving business success.

Job Description

Key Responsibilities:

• Understand complex and critical business problems, formulates integrated analytical approach to mine data sources, employ statistical methods and machine learning algorithms to contribute to solving unmet medical needs, discover actionable insights, and automate processes for reducing effort and time for repeated use.
• Architect and develop end-to-end AI/ML and Gen AI solutions, focusing on scalability, performance, and modularity while ensuring alignment and best practices with enterprise architecture standards.
• Manage the implementation and adherence to the overall data lifecycle of enterprise data from data acquisition or creation through enrichment, consumption, retention, and retirement, enabling the availability of useful, clean, and accurate data throughout its useful lifecycle.
• High agility to be able to work across various business domains. High agility to be able to work across various business domains. Integrate business presentations, smart visualization tools and contextual storytelling to translate findings back to business users with a clear impact.
• Independently manage budget, ensuring appropriate staffing and coordinating projects within the area.
• Collaborate with globally dispersed internal stakeholders and cross-functional teams to solve critical business problems and deliver successfully on high visibility strategic initiatives.

Essential Requirements
 

Education & Qualifications

• Advanced degree in Computer Science, Engineering, or a related field (PhD preferred).

Experience

• 8+ years of experience in AI/ML engineering (data engineering could be appropriate depending on experience), with at least 2 years focusing on designing and deploying LLM-based solutions.
• Strong proficiency in building AI/ML architectures and deploying models at scale with experience in cloud computing platforms such as AWS, Google Cloud, or Azure.
• Deep knowledge of LLMs and experience in applying them in business contexts.
• Knowledge of containerization technologies (Docker, Kubernetes) and CI/CD pipelines Hands-on experience with cloud platforms (AWS, Azure, GCP) and MLOps tools for scalable deployment.
• Experience with API development, integration, and model deployment pipelines.
• Strong problem-solving skills and a proactive, hands-on approach to challenges.
• Ability to work effectively in cross-functional teams and communicate technical concepts clearly.
• Excellent organizational skills and attention to detail in managing complex systems.

Commitment to Diversity and Inclusion:
Novartis is committed to building an outstanding, inclusive work environment and diverse teams' representative of the patients and communities we serve.
Accessibility and accommodation
Novartis is committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the recruitment process, or in order to perform the essential functions of a position, please send an e-mail to diversityandincl.india@novartis.com and let us know the nature of your request and your contact information. Please include the job requisition number in your message

Skills Desired

Apache Hadoop, Applied Mathematics, Big Data, Curious Mindset, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Deep Learning, Generative AI, Large Language Models (LLMs), Machine Learning (ML), Machine Learning Algorithms, Master Data Management, Proteomics, Python (Programming Language), R (Programming Language), Statistical Modeling