
Senior AL/ML Ops Engineer-II (Hybrid in Bangalore)
필수 스킬
AWS
Docker
Kubernetes
For over 20 years, Smartsheet has helped people and teams achieve–well, anything. From seamless work management to smart, scalable solutions, we’ve always worked with flow. We’re building tools that empower teams to automate the manual, uncover insights, and scale smarter. But more than that, we’re creating space– space to think big, take action, and unlock the kind of work that truly matters. Because when challenge meets purpose, and passion turns into progress, that’s magic at work, and it’s what we show up for everyday.
Our India Global Capability Center isn't just supporting global operations—we’re leading global innovation. After scaling rapidly into a best-in-class hub, we deliver the product innovation and enterprise capabilities that accelerate our global growth, profitability, and scale. As we expand Smartsheet India, we’re searching for Senior AI/ML Ops Engineers who crave variety and ownership. You’ll have the opportunity to work across multiple teams and disciplines, building a versatile skillset while solving the complex challenges of a global platform.
You Will:
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Designing, Developing and overseeing the strategy and architecture of scalable and reliable AI/ML Ops platforms / pipelines
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Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable
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CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools
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Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms
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Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools.
Experience in Monte Carlo is preferable:
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Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time.
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Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with AWS Bedrock is preferable
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Resource Optimization: Manage GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference for users
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Collaboration: Work closely with data scientists, data engineers, and software engineers to bridge the gap between model development and production.
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Version Control & Governance: Manage versioning for data, code, and models using tools like MLflow.
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Security & Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data
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Technology Evaluation and Innovation: Staying abreast of emerging data technologies and exploring opportunities for innovation to improve the organisation’s data infrastructure
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Troubleshooting and Problem Solving: Diagnosing and resolving complex data-related issues, ensuring the stability and reliability of the data platform
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Perform other duties as assigned
You Have:
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Enterprise SaaS software solutions with high availability and scalability
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Solution handling large scale structured and unstructured data from varied data sources
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Experience in building and maintaining AI/ML Ops platform systems ensuring scalability, reliability, efficiency and security
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Working with Product engineering team to influence designs with data, AI and analytics use cases in mind
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In depth experience in System design, AI/ML Frameworks and tools involving large Petabytes of data with Databricks Lakehouse ecosystem
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AI/MLOps workflows on Databricks , MLFlow, Mosaic AI Agent Framework, Unity Catalog, Vector Search, Knowledge Graph
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Knowledge of AI/ML frameworks like Lang Chain, Lang Graph for AI/ML Ops pipeline integration
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Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). Experience in AWS hosted data platform is preferable
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Programming languages like Python and SQL
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Modern software engineering practices like Kubernetes, CI/CD, IAC tools (Preferably Terraform), Observability, monitoring and alerting
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Solution Cost Optimisations and design to cost
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Legally eligible to work in India on an ongoing basis
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, and India. 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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Smartsheet 소개

Smartsheet
PublicSmartsheet provides cloud-based work management and collaboration software that allows teams to organize, track, and automate work processes. The platform combines spreadsheet functionality with project management, workflow automation, and business intelligence capabilities.
1,001-5,000
직원 수
Bellevue
본사 위치
$4.5B
기업 가치
리뷰
10개 리뷰
3.8
10개 리뷰
워라밸
3.2
보상
2.5
문화
4.1
커리어
4.0
경영진
3.0
72%
지인 추천률
장점
Flexible hours/schedule
Supportive management and team
Growth and learning opportunities
단점
Heavy workload and high expectations
Compensation could be better
Limited remote work options
연봉 정보
19개 데이터
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Senior/L5
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2개 리포트
$154,000
총 연봉
기본급
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주식
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보너스
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$154,000
$154,000
면접 후기
후기 2개
난이도
3.0
/ 5
면접 과정
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
5
Onsite/Virtual Interviews
6
Offer
자주 나오는 질문
Coding/Algorithm
Technical Knowledge
Behavioral/STAR
Past Experience
Culture Fit
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