Senior Machine Learning Operations Engineer
Smartsheet is hiring a Senior Machine Learning Operations Engineer in Bengaluru, India. Level rates it ; you can apply on Smartsheet.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- The posting mentions OpenAI, LangChain and Hugging Face among its tools and skills.
- Level classes it as a senior role.
- Smartsheet has 88 other open roles on Level.
AI in this role
Architect and maintain scalable MLOps infrastructure, deployment pipelines, and monitoring systems for machine learning models.
For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day.
Smartsheet is hiring a Senior Machine Learning Operations Engineer to architect our machine learning production lifecycle. Your mission is to maintain and deploy ML models to a scalable, reliable, and secure production environment. You will design and maintain the infrastructure, automation, and monitoring systems that ensure our AI products are high-performing and cost-effective.
You will report to our Director, Analytics Engineering & Data Governance and work from our Bangalore, India office.
You Will:
Model and Pipeline Automation
- Automate the deployment and retraining of ML models, from training through to production inference, by building and managing complete CI/CD/CT (Continuous Training) pipelines, adhering to MLOps best practices.
- Build, fine-tune, or use pre-trained LLMs, deep learning models or traditional machine learning models.
- Evaluate and recommend AI or ML solutions for the product using any combination of vendor solutions and/or custom-built models.
Governance & Compliance
- Implement model versioning, lineage tracking, and auditing to ensure compliance with security and ethical standards.
Performance Monitoring
- Continuously monitor the health and performance of production machine learning models, proactively identifying and correcting model drift, staleness, and performance degradation.
- Incorporate user feedback for iterative improvements and manage necessary model retraining cycles.
Cross-Functional Collaboration
- Act as the "glue" between Data Scientists (who build models) and Software Engineers (who consume them).
- Partner effectively with software engineers, product managers and business functions to integrate the machine learning solutions across smartsheet.
Architecture and Infrastructure Management
- Provision and manage scalable cloud infrastructure using Infrastructure as Code (IaC).
- Provide architectural guidance and mentorship to a team consisting of ML engineers, data scientists and analytics engineers.
- Distill complex ML concepts into easy-to-follow technical documentation.
You Have:
- 5+ years of experience with creating, deploying and scaling machine learning solutions in a cloud environment (eg. AWS, GCP, Azure) and ability to use tools such as SageMaker, Glue, Lambda, Docker etc. to create ML models and data pipelines.
- 7+ years of programming experience in languages used in AI/ML (eg python, scala etc)
- 4+ years of experience in developing deep learning and traditional ML models using common frameworks like pytorch, tensorflow, huggingface, scikit-learn etc.
- Strong applied data science skills - ability to recognize data patterns, understand how and when to use various machine learning approaches (eg. supervised/unsupervised learning, deep learning etc.), and evaluate the performance of ML algorithms.
- Proven ability to remain up-to-date with the latest advancements in Generative AI approaches (eg. OpenAI, LangChain, Stable Diffusion APIs).
- Experience developing, documenting, and supporting REST APIs
- A degree in Computer Science, Engineering, or a related field or equivalent practical experience.
Get to Know Us:
At Smartsheet, your ideas are heard, your potential is supported, and your contributions have real impact. You’ll have the freedom to explore, push boundaries, and grow beyond your role. We welcome diverse perspectives and nontraditional paths—because we know that impact comes from individuals who care deeply and challenge thoughtfully. When you’re doing work that stretches you, excites you, and connects you to something bigger, that’s magic at work. Let’s build what’s next, together.
Equal Opportunity Employer:
Smartsheet is an Equal Opportunity (EEO) employer committed to fostering an inclusive environment with the best employees. It is our policy to provide equal employment opportunities to all qualified applicants in accordance with applicable laws in the US, UK, Australia, Germany, Costa Rica, Japan, Bulgaria, India, and Singapore. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information.
If there are preparations we can make to help ensure you have a comfortable and positive interview experience, please let us know.
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How we rate this
Senior Machine Learning Operations Engineer at Smartsheet rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- Tell me about a project where mlops was part of your work. What did you do?
- Tell me about a project where infrastructure as code was part of your work. What did you do?
- Tell me about a project where model monitoring was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI agents, ML Ops, MLOps, Infrastructure As Code, and Model Monitoring. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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