Smartsheet
About this role
This is the platform half of machine learning at Smartsheet: you own the strategy and architecture of
scalable, reliable AI and ML Ops platforms and pipelines. The work runs from packaging and deploying
models to production so they are reproducible and interpretable, through designing automated CI/CD
pipelines that speed up deployment, to provisioning and optimising the training and serving
infrastructure on Docker, Kubernetes or serverless. Post deployment monitoring is explicitly part of
it, watching model performance, data drift and latency, and automating retraining so models stay
accurate. You also manage deployment of foundation models. Apply if you want the engineering side of
ML, which is where most models actually fail, and if Databricks and AWS Bedrock are familiar ground.
scalable, reliable AI and ML Ops platforms and pipelines. The work runs from packaging and deploying
models to production so they are reproducible and interpretable, through designing automated CI/CD
pipelines that speed up deployment, to provisioning and optimising the training and serving
infrastructure on Docker, Kubernetes or serverless. Post deployment monitoring is explicitly part of
it, watching model performance, data drift and latency, and automating retraining so models stay
accurate. You also manage deployment of foundation models. Apply if you want the engineering side of
ML, which is where most models actually fail, and if Databricks and AWS Bedrock are familiar ground.
Who this is for
⚠️ This posting states no clear years requirement, so the figure is left empty rather than guessed and
this row appears in no experience band on the page filter. Note that a parser scanning for a number
before the word "years" finds Smartsheet's standard opening line, "For over 20 years, Smartsheet has
empowered teams", which is the company's age and nothing to do with the candidate. That is exactly the
kind of trap that makes reading by hand necessary. Smartsheet's separate Senior MLOps requisition asks
5+, 7+ and 4+ years across three bullets, so this family of roles sits in that region.
Requirements. Hands-on experience with at least one major cloud provider (AWS, Azure or GCP).
Experience building and maintaining AI and ML Ops platform systems for scalability, reliability,
efficiency and security. In depth experience in system design and AI/ML frameworks and tools involving
petabytes of data within the Databricks Lakehouse ecosystem. Experience with Monte Carlo is
preferable. Experience with AWS Bedrock is preferable.
What you would do. Design, develop and oversee the strategy and architecture of scalable and reliable
AI/ML Ops platforms and pipelines. Package and deploy AI/ML services to production, ensuring they are
reproducible and interpretable. Design and implement automated CI/CD pipelines to accelerate model
deployment. Provision and optimise infrastructure for training and serving using Docker, Kubernetes or
serverless platforms. Implement post deployment monitoring for model performance, data drift and
latency. Automate retraining and data pipeline workflows so models stay accurate. Manage the
deployment of foundation models.
⛔ Ignore the remote tag. The title says "Hybrid in Bangalore" and the posting carries an #LI-Remote
tag anyway. Every Smartsheet India requisition checked across two editions has the same contradiction.
The title is right.
Who this is for. An ML platform engineer who wants ownership of architecture rather than model
building. Databricks Lakehouse depth is the distinguishing requirement.
this row appears in no experience band on the page filter. Note that a parser scanning for a number
before the word "years" finds Smartsheet's standard opening line, "For over 20 years, Smartsheet has
empowered teams", which is the company's age and nothing to do with the candidate. That is exactly the
kind of trap that makes reading by hand necessary. Smartsheet's separate Senior MLOps requisition asks
5+, 7+ and 4+ years across three bullets, so this family of roles sits in that region.
Requirements. Hands-on experience with at least one major cloud provider (AWS, Azure or GCP).
Experience building and maintaining AI and ML Ops platform systems for scalability, reliability,
efficiency and security. In depth experience in system design and AI/ML frameworks and tools involving
petabytes of data within the Databricks Lakehouse ecosystem. Experience with Monte Carlo is
preferable. Experience with AWS Bedrock is preferable.
What you would do. Design, develop and oversee the strategy and architecture of scalable and reliable
AI/ML Ops platforms and pipelines. Package and deploy AI/ML services to production, ensuring they are
reproducible and interpretable. Design and implement automated CI/CD pipelines to accelerate model
deployment. Provision and optimise infrastructure for training and serving using Docker, Kubernetes or
serverless platforms. Implement post deployment monitoring for model performance, data drift and
latency. Automate retraining and data pipeline workflows so models stay accurate. Manage the
deployment of foundation models.
⛔ Ignore the remote tag. The title says "Hybrid in Bangalore" and the posting carries an #LI-Remote
tag anyway. Every Smartsheet India requisition checked across two editions has the same contradiction.
The title is right.
Who this is for. An ML platform engineer who wants ownership of architecture rather than model
building. Databricks Lakehouse depth is the distinguishing requirement.
Apply on company site
Opens job-boards.greenhouse.io, the employer's own application page. Applying is always free.