4 days ago
Hyderābād, IndiaStaff+
Responsibilities
- Own the AI infrastructure reference architecture and governance standards for training, fine-tuning, inference, and retrieval-augmented workloads.
- Define the AI workload intake process, architecture review criteria, data classification handling, environment selection, and production-readiness conditions.
- Set guardrails for sensitive data, model access, prompt and inference logging, monitoring, resilience, retention, cost, and risk management.
- Design GPU and accelerator capacity patterns, including shared pools, reservations, scheduling, quotas, cloud bursting, and related cost models.
- Qualify AI platforms, regions, and services for enterprise use with AI and HPC engineering and public cloud architects.
- Define MLOps and LLMOps architecture for model registries, versioning, evaluation, deployment, drift monitoring, performance monitoring, and decommissioning.
- Maintain architecture decision records tied to business value, risk posture, and total cost of ownership.
- Advise business, research, and infrastructure stakeholders and translate scientific and commercial requirements into technical designs.
- Evaluate emerging accelerators, model-serving stacks, and platform services and incorporate credible innovation into the roadmap.
- Coach engineers and architects on AI infrastructure practices.
Requirements
- At least 8 years of experience in infrastructure, cloud, or platform architecture with a strong enterprise-scale technology delivery record.
- Demonstrated experience building, deploying, and operating AI and machine learning infrastructure in production environments.
- Experience moving AI solutions from proof of concept to scalable, governed, business-critical services.
- Strong knowledge of GPU and accelerator technologies, Kubernetes, container orchestration, Azure, and AWS.
- Hands-on experience with model serving, inference optimization, MLOps/LLMOps, and modern data and vector pipeline architectures.
- Understanding of identity and access management, observability, FinOps, cost optimization, and secure AI platform operations.
- Familiarity with Responsible AI principles such as explainability, auditability, logging, human oversight, and risk controls.
- Strong stakeholder management and communication skills across engineering, security, privacy, and business teams.
- Fluent written and spoken English; additional languages are an asset.
Benefits
- Benefits and rewards are provided through the Novartis Life Handbook.
- The position is an individual contributor architecture role; no specific remote, hybrid, or office arrangement is stated.
Tech Stack
Categories
About Novartis
Novartis is an innovative medicines company. Every day, working to reimagine medicine to improve and extend people’s lives so that patients, healthcare professionals and societies are empowered in the face of serious disease. Our medicines reach more than 250 million people worldwide. Find out more at https://www.novartis.com See our community guidelines: https://go.novartis.social/3Nboxki