Senior Machine Learning Operations Engineer

This is the MLOps seat at Smartsheet, and the posting describes it well: you are the glue between the
data scientists who build models and the software engineers who consume them. You automate deployment
and retraining end to end, building full CI/CD/CT pipelines so models go from training to production
inference without hand holding, then watch them in production for drift, staleness and degradation
and retrain when they slip. You also build, fine tune or apply pre-trained LLMs, deep learning and
traditional models, and decide when a vendor solution beats a custom one. Governance is explicitly
part of it: model versioning, lineage tracking and auditing for security and ethical compliance.
Apply if you like the operational half of machine learning, which is where most models actually fail.

Requirements. 5+ years of experience creating, deploying and scaling machine learning solutions in a
cloud environment. 7+ years of programming experience in languages used in AI/ML such as Python or
Scala. 4+ years of experience developing deep learning and traditional ML models using common
frameworks such as PyTorch, TensorFlow, HuggingFace or scikit-learn. Hands-on experience with at
least one major cloud provider (AWS, Azure or GCP). Legally eligible to work in India on an ongoing
basis.

⚠️ Three different year figures. The requirement list carries 5+, 7+ and 4+ years across three
separate bullets, all mandatory. We publish the highest mandatory figure, 7, because that is the bar
you have to clear on the programming requirement. Any tool taking the smallest number would list this
as a 4 year job, which would be wrong.

What you would do. Automate deployment and retraining of ML models from training through production
inference by building and managing complete CI/CD/CT pipelines. Build, fine tune or use pre-trained
LLMs, deep learning models or traditional ML models. Evaluate and recommend AI or ML solutions using
vendor products, custom models or both. Implement model versioning, lineage tracking and auditing for
compliance with security and ethical standards. Continuously monitor production model health,
identifying and correcting drift, staleness and degradation. Incorporate user feedback and manage
retraining cycles. Act as the connection between data scientists and software engineers. Partner with
engineers, product managers and business functions to integrate ML across Smartsheet. Provision and
manage scalable cloud infrastructure using infrastructure as code. Provide architectural guidance and
mentorship to ML engineers, data scientists and analytics engineers. Write clear technical
documentation.

Reporting line and location. You report to the Director, Analytics Engineering and Data Governance,
and the posting says you "work from our Bangalore, India office".

⛔ Ignore the remote tag. Like every Smartsheet India posting in this edition, this one carries an
#LI-Remote tag while the body states a Bangalore office. The body wins.

Who this is for. An ML engineer with seven or more years who wants the platform and operations side.
The mentorship line is architectural guidance, not people management.