Associate Director AI & GenAI Engineering

Job Description Summary Internal Job Title: Associate Director DDIT AI & GenAI Engineering #LI-Hybrid Location: Hyderabad, India Relocation Support: Yes Are you passionate about shaping the future of Artificial Intelligence at global scale? Join Novartis and play a pivotal role in transforming how innovative medicines are discovered, developed, and delivered. As Associate Director AI & GenAI Engineering, you will lead the design, engineering, and delivery of cutting-edge Generative Artificial Intelligence and agentic AI solutions that drive meaningful business outcomes and improve patient lives. Working at the intersection of advanced technology, enterprise platforms, and scientific innovation, you will help turn bold ideas into production-ready solutions while influencing the future of AI across Novartis. Job Description Key Responsibilities Lead end-to-end delivery of enterprise AI and GenAI solutions from concept through production. Define scalable AI architectures, platform capabilities, engineering standards, and technology roadmaps. Partner with business stakeholders to transform workflows into intelligent, AI-enabled experiences. Drive rapid prototyping and minimum viable product development for high-value business opportunities. Provide technical leadership across generative artificial intelligence, agentic AI, machine learning, and semantic search. Design and optimize AI platforms, retrieval-augmented generation pipelines, APIs, and cloud-native applications. Establish engineering best practices for automation, observability, continuous integration, and continuous delivery. Ensure solutions meet security, privacy, regulatory, reliability, performance, and responsible AI standards. Manage delivery plans, dependencies, risks, budgets, and stakeholder communications across initiatives. Mentor engineering teams and foster a culture of innovation, technical excellence, and continuous learning. Essential Requirements Bachelor’s degree in Computer Science, Artificial Intelligence, Engineering, or a related field. 12+ years of experience in software engineering, artificial intelligence, machine learning, or digital technology. Proven experience leading artificial intelligence engineering teams and complex enterprise delivery programs. Strong expertise in generative artificial intelligence, large language models, retrieval-augmented generation, and agentic AI. Hands-on experience developing Python applications, application programming interfaces, microservices, and cloud-native solutions. Experience with Amazon Web Services or Microsoft Azure artificial intelligence services and enterprise data platforms. Strong knowledge of machine learning operations, large language model operations, DevSecOps, continuous integration, and continuous delivery. Demonstrated ability to manage senior stakeholders, delivery risks, vendors, budgets, and cross-functional teams. Desirable Requirements Master’s degree in Computer Science, Artificial Intelligence, Engineering, or a related field. Professional certifications in cloud, artificial intelligence, machine learning, or software engineering technologies. 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 Artificial Intelligence (AI), Biostatistics, Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis