Sr. Platform Engineer

About the Role

We are building the foundational technology layer for Novartis’ next-generation AI/ML-driven Advanced Analytics capability. As a Platform Engineer, you will play a pivotal role in setting up the technical scaffolding that enables data scientists and AI engineers to scale their efforts rapidly and securely. This role requires

hands-on engineering depth, a strategic mindset, and a passion for enabling AI-driven transformation in the healthcare domain.

Key Responsibilities

• Foundational Platform Setup: Design and build secure, scalable, and resilient data & ML platforms to enable rapid AI experimentation and deployment.

• Cloud & DevOps Enablement: Set up and optimize infrastructure on AWS (SageMaker, EKS, Lambda, Glue, Step Functions, Fargate) and Azure DevOps pipelines. Implement CI/CD workflows, monitoring, and logging for MLOps.

• Build-to-Destroy Cycle Management: Own the end-to-end environment lifecycle including provisioning, destruction, and rebuild of infrastructure and pipelines.

• Scalable Orchestration & Compute: Design for distributed computing, dynamic late binding, and dependency injection across hybrid cloud environments.

• Data Pipeline Engineering: Collaborate with data engineers and scientists to deploy and optimize high performance data pipelines using modern orchestration tools.

• Cross-Functional Collaboration: Partner with AI engineers, DevSecOps, data scientists, and delivery managers to ensure fast onboarding and alignment with business objectives.

Technical Skills & Requirements

• Cloud Expertise: Deep experience in AWS services (EKS, SageMaker, Lambda, Glue, Step Functions, Fargate)

• Working knowledge of Azure, particularly Azure DevOps and integration tooling

• DevOps & Infrastructure as Code:

• Proficient in Terraform, Helm, Docker, Kubernetes

• Experience with CI/CD using GitHub Actions, Jenkins, or Azure DevOps

• Engineering Fundamentals:

• Strong in distributed computing architecture

• Hands-on understanding of dependency injection and late-binding systems

• Strong scripting (Python, Bash) and automation mindset

• ETL & Data Engineering Acumen:

• Background in healthcare, life sciences, or related regulated industries

• Experience building ML/AI platforms for model deployment, experimentation tracking, and governance

• Exposure to hybrid or multi-cloud environments