Senior AI/ML ops engineer at Smartsheet in Bangalore (hybrid): designing and running the platforms and pipelines that take AI/ML services to production reliably.

This Bangalore role was posted on 30 Sep 2026 and was still open when we checked on 31 October. The team designs, develops and oversees the strategy and architecture of scalable, reliable AI/ML ops platforms: model deployment, CI/CD, infrastructure on Docker and Kubernetes, and monitoring for model performance, data drift and latency. The listing asks for experience with enterprise SaaS at high availability, large-scale structured and unstructured data, and the Databricks Lakehouse.

About Smartsheet

Smartsheet is the Intelligent Work Management Platform: for over 20 years it has helped teams plan, track and automate work, and it is now uniting people, data and AI - including AI agents - in one platform. Its India Global Capability Center in Bangalore is described as leading global product innovation, not just supporting operations. Learn more on Smartsheet's website.

The role in plain terms

You build and operate the ML platform: packaging and deploying reproducible, interpretable AI/ML services; designing automated CI/CD pipelines; provisioning training and serving infrastructure with Docker, Kubernetes and infrastructure-as-code; and implementing post-deployment monitoring for drift, latency and performance. The listing also asks you to influence product engineering designs with data, AI and analytics use cases in mind.

What you will do

Per the employer's listing, you will:

7-12 Years of Experience in Designing, developing and maintaining stable and reliable AI/ML Ops platforms / pipelines
Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable
CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools
Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms
Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable

What the employer asks for

These requirements are quoted from the employer's listing, kept in their exact words so eligibility details are not distorted:

Enterprise SaaS software solutions with high availability and scalability
Solution handling large scale structured and unstructured data from varied data sources
Experience in building and maintaining AI/ML Ops platform systems ensuring scalability, reliability, efficiency and security
Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). Experience in AWS hosted data platform is preferable
Programming languages like Python and SQL
Modern software engineering practices like Kubernetes, CI/CD, IAC tools (Preferably Terraform), Observability, monitoring and alerting

Who this role suits

ML ops and platform engineers with production deployment experience - the listing names Kubernetes, CI/CD, IaC (preferably Terraform), observability and the Databricks Lakehouse specifically. Strong fit for SRE/DevOps engineers who moved into ML platforms, or ML engineers who own their own infra. Not for notebook-only data scientists.

Practical notes

Prepare to discuss a model deployment pipeline you owned end to end: packaging, rollout, monitoring and what broke. Databricks Lakehouse experience is named - flag yours clearly. Hybrid in Bangalore per the listing.

How to apply

Apply directly on the employer's site: Senior AI/ML Ops Engineer at Smartsheet. We found this listing on the Greenhouse public job board (job-boards.greenhouse.io), where it was posted on 30 Sep 2026 and was still open when we checked on 31 Oct 2026. Before applying, re-read the employer's own description in full, since eligibility details, deadlines and process steps can change at any time and the employer's page is always the final word. Tailor your resume to the specific requirements above rather than sending a generic one: mirror the skills and tools the listing names where you genuinely have them. A short, specific note on why this team and this problem interest you goes further than a long generic cover letter. Never pay a fee to apply or interview; genuine employers, including this one, do not charge candidates.

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