Job Description Summary

Job Title: Senior Expert (AI-Engineering)
Location: Hyderabad, India
#LI-Hybrid

Relocation Support: Yes

Imagine shaping how breakthrough medicines are discovered by bringing cutting-edge AI from concept to real-world application. As a Senior Expert in AI-Engineering, you will sit at the critical intersection of innovation and delivery—transforming advanced machine learning models into scalable, reliable solutions that accelerate drug discovery. Collaborating with leading scientists and AI experts, you will drive the translation of pioneering research into impactful tools, ensuring models are reproducible, efficient, and ready to solve complex biomedical challenges. This is an opportunity to directly influence how modern AI reshapes scientific discovery and improves patient outcomes.

 

Job Description

Key Responsibilities

• Design and train advanced machine learning models for drug discovery, including deep learning and graph-based architectures
• Own end-to-end model lifecycle from problem definition to production deployment and continuous improvement
• Conduct rigorous benchmarking, validation, and performance evaluation against scientific and business objectives
• Deploy scalable, reliable model inference services meeting latency, throughput, and cost requirements
• Build automated training, retraining, and monitoring pipelines including model drift detection mechanisms
• Optimize models and pipelines for performance, reliability, and scalability across production environments
• Establish and promote engineering best practices for reproducibility, testing, and deployment standards
• Collaborate with cross-functional teams to translate research innovations into production-ready AI solutions
• Partner with scientific experts to validate models against biological and chemical benchmarks
• Mentor teams and contribute to raising engineering excellence across the AI for Research organization

Essential Requirements

• Minimum four years of experience building, training, and deploying machine learning models with measurable real-world impact
• Deep proficiency in Python and frameworks such as PyTorch or JAX; expertise in model design and optimization
• Proven experience fine-tuning and deploying large pre-trained and foundation models including language and scientific models
• Strong understanding of model evaluation including benchmarking, cross-validation, and performance across data distributions
• Experience with distributed training and large-scale computing environments for high-performance model training
• Hands-on expertise optimizing models and pipelines for latency, throughput, cost, and production performance
• Experience with MLOps practices including experiment tracking, model versioning, reproducibility, and cloud deployment
• Strong problem-solving, collaboration, and communication skills with ability to drive engineering excellence in complex environments

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 [email protected] 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, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Logistic Regression Model (Inactive), Machine Learning (ML), Machine Learning Algorithms, Nlp (Neuro-Linguistic Programming) And Genai (Inactive), Pandas (Python) (Inactive), Python (Programming Language), R Programming, Stakeholder Engagement, Statistical Analysis, Structured Query Language (SQL), Time Series Analysis