Associate Director - AI Infrastructure Architect AI Factory

Novartis India; Hyderabad; On-site; INR 4,200,000 - 7,000,000; Full time

Job summary

Novartis India is seeking an AI Infrastructure Architect to define reference architectures for AI workloads across Azure, AWS, and private hosting. The role focuses on training, inference, and cost governance with a strong emphasis on scalable, secure AI platforms.

You will guide data pipelines, model operations, and governance while collaborating with security, privacy, and business stakeholders to deliver value and risk-managed AI solutions.

Qualifications

• 8+ years in infrastructure/cloud/platform architecture for enterprise-scale solutions.
• Experience building, deploying, and operating AI/machine learning infrastructure in production.
• Experience moving AI solutions from PoC to scalable, governed production services.
• Strong knowledge of GPUs/accelerators, Kubernetes, and cloud AI platforms (Azure/AWS).
• Hands-on with model serving, inference optimization, MLOps/LLMOps, and modern data/vector pipelines.
• Understanding of IAM, observability, FinOps, and secure AI platform operations.
• Familiarity with Responsible AI principles, explainability, auditability, logging, and risk controls.
• Excellent stakeholder management and ability to communicate complex AI infra concepts across teams.

Responsibilities

• Own the AI infrastructure reference architecture and the standards for training, fine-tuning, inference and retrieval-augmented patterns across public cloud and private hosting.
• Define the standard AI workload intake pattern including review criteria, data classification, environment choice and production conditions.
• Set guardrails for data, model access, logging, monitoring, resilience, retention and cost with Security and Privacy partners.
• Design GPU and accelerator capacity patterns, shared pools, reservations, scheduling, quotas and cloud burst, plus costing models.
• Support qualification of AI platforms, regions and services for enterprise consumption with AI & HPC Engineering and cloud architects.
• Define the MLOps/LLMOps reference chain: model registry, versioning, evaluation, deployment, drift and monitoring, decommissioning.
• Maintain architecture decision records linking choices to business value, risk posture and total cost of ownership.
• Advise stakeholders on feasible AI infrastructure options and translate scientific/business needs into technical designs.
• Track AI infrastructure market trends and bring credible innovation into the roadmap.
• Coach engineers and architects on AI infra practices and embody Novartis values.

Skills

Kubernetes

MLOps

LLMOps

GPU Acceleration

Azure

AWS

Data Pipelines

FinOps

Tools

Blockchain?

Job description

Job Description:

Summary

Novartis 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.

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)