Associate Director - AI Infrastructure Architect (AI Factory)

Novartis India; Hyderabad; On-site; INR 6,000,000 - 7,500,000; Full time

Job summary

Novartis India is seeking an AI Infrastructure Architect to define and govern the reference architecture for AI workloads across Azure, AWS, and private environments. You will shape training, inference, and data pipelines, ensuring secure, cost-controlled, and scalable solutions across on-prem and cloud hosting.

The role emphasizes collaboration with Security, Privacy, and Quality teams, along with driving governance, monitoring, and lifecycle management of AI platforms in production

Qualifications

• 8+ years' experience in infrastructure, cloud, or platform architecture.
• Experience building, deploying, and operating AI/ML infrastructure in production.
• Experience scaling proof-of-concept to production-scale services.
• Strong knowledge of GPU/accelerator tech and Kubernetes.
• Hands-on with model serving, inference optimization, and MLOps/LLMOps.
• Secure AI platform operations, IAM, and cost optimization (FinOps).
• Familiarity with Responsible AI principles including explainability and auditing.
• Excellent stakeholder management and cross-team collaboration.

Responsibilities

• Own the AI infrastructure reference architecture and standards for training, inference, and retrieval patterns across public cloud and private hosting.
• Define standard AI workload intake, architecture review criteria, data classification, and production readiness conditions.
• Set guardrails for data, model access, logging, monitoring, cost, and resilience with Security and Privacy teams.
• Design GPU/accelerator capacity patterns, scheduling, quotas, and cost models.
• Qualify AI platforms, regions, and services with AI & HPC Engineering and public cloud architects.
• Define MLOps/LLMOps reference chain: registry, versioning, deployment, drift monitoring, decommissioning.
• Maintain architecture decision records linking to business value and total cost of ownership.
• Advise stakeholders on viable AI infrastructure options and translate requirements into technical designs.

Skills

Cloud architecture

AI infrastructure

Kubernetes

GPU technology

MLOps/LLMOps

Security

FinOps

Stakeholder management

Tools

Azure

AWS

Job description

SummaryNovartis architecture principles call for AI adoption guided by business value: AI is evaluated for new solutions and enhancements wherever it delivers a clear benefit, and it must be delivered on infrastructure that is secure, compliant and economically sustainable. Within Cloud & Hosting Services, AI & HPC Engineering is the single product management and delivery channel for AI infrastructure and AI use-case onboarding. The AI Infrastructure Architect sets the architecture those platforms are built to.

SummaryNovartis architecture principles call for AI adoption guided by business value: AI is evaluated for new solutions and enhances wherever it delivers a clear benefit, and it must be delivered on infrastructure that is secure, compliant and economically sustainable. Within Cloud & Hosting Services, AI & HPC Engineering is the single product management and delivery channel for AI infrastructure and AI use-case onboarding. The AI Infrastructure Architect sets the architecture those platforms are built to. The role defines the reference architecture and guardrails for AI workloads across Azure, AWS and private AI environments: training and inference platforms, GPU capacity and scheduling, data pipelines and data access, model operations, logging, monitoring, cost control and lifecycle governance. It establishes the standard intake pattern by which an AI use case moves from idea to a hosted, monitored, auditable service, so that scientists and business teams get speed without the organisation inheriting uncontrolled risk. This is an individual contributor architecture role. Its authority comes from design quality, evidence and adoption rather than from line management.

About The Role

Key Responsibilities

• Own the AI infrastructure reference architecture and the standards that govern training, fine-tuning, inference and retrieval-augmented patterns across public cloud and private AI hosting.
• Define the standard AI workload intake pattern, including architecture review criteria, data classification handling, environment selection and the conditions under which a use case may go to production.
• Set guardrails for sensitive data, model access, prompt and inference logging, monitoring, resilience, retention and cost, in partnership with Security, Data Privacy and Quality.
• Design GPU and accelerator capacity patterns - shared pools, reservations, scheduling, quotas and burst to cloud - and support the associated capacity and cost models.
• Support qualification of AI platforms, regions and services for enterprise consumption together with AI & HPC Engineering and the public cloud architects.
• Define the MLOps and LLMOps reference chain: model registry, versioning, evaluation, deployment, drift and performance monitoring, and decommissioning.
• Maintain architecture decision records for AI platform choices, linking each to business value, risk posture and total cost of ownership.
• Advise business, research and Infrastructure Solution Delivery stakeholders on feasible AI infrastructure options, and translate scientific and commercial requirements into technical designs.
• Track the AI infrastructure market, evaluate emerging accelerators, model-serving stacks and platform services, and bring credible innovation into the roadmap.
• Coach engineers and architects on AI infrastructure practice and act as a role model for the Novartis Values and Behaviours.

Essential Requirement

• 8+ years' experience in infrastructure, cloud, or platform architecture, with a strong track record of designing and supporting enterprise-scale technology solutions.
• Demonstrated expertise in building, deploying, and operating AI and machine learning infrastructure within production environments.
• Experience enabling AI solutions to transition from proof-of-concept stages into scalable, governed, and business-critical services.
• Strong knowledge of GPU and accelerator technologies, Kubernetes, container orchestration, and cloud-based AI platforms such as Azure and AWS.
• Hands‑on experience with model serving, inference optimization, MLOps/LLMOps practices, and modern data and vector pipeline architectures.
• Solid understanding of identity and access management, observability, cost optimization (FinOps), and secure AI platform operations.
• Familiarity with Responsible AI principles, including explainability, auditability, logging, human oversight, and risk management controls.
• Excellent stakeholder management and communication skills, with the ability to explain complex AI infrastructure concepts to technical and non-technical audiences while driving collaboration across engineering, security, privacy, and business teams.

Desirable Requirement

• Fluent English, written and spoken. Additional languages are an asset.

You’ll receive

You can find everything you need to know about our benefits and rewards in the Novartis Life Handbook. https://www.novartis.com/careers/benefits-rewards

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.

Why Novartis

Helping people with disease and their families takes more than innovative science. It takes a community of smart, passionate people like you. Collaborating, supporting and inspiring each other. Combining to achieve breakthroughs that change patients’ lives. Ready to create a brighter future together? https://www.novartis.com/about/strategy/people-and-culture

Benefits and Rewards

Learn about all the ways we’ll help you thrive personally and professionally. Read our handbook (PDF 30 MB)