Senior MLOps Engineer
Hyderabad, Telangana, India
- Pay
- Salary not listed in the saved posting
- Work setup
- Unconfirmed
- Employment
- Unconfirmed
What you’ll work on
Full postingCrunchyroll is growing and changing, presenting unique challenges and opportunities to support millions of anime fans around the world.
Design, build, and maintain end-to-end ML infrastructure and pipelines to support model training, deployment, and monitoring.
What you’ll bring
All qualificationsCore experience
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field.
- 8+ years of experience in MLOps, ML infrastructure, or DevOps for AI/ML systems.
- Deep knowledge of CI/CD and automation frameworks (GitHub Actions, Terraform, CloudFormation).
- Hands-on experience with containerization (Docker) and orchestration (EKS).
- Proficiency in Python and scripting for ML integrations.
- Strong knowledge of cloud platforms (AWS preferred) and services relevant to ML (SageMaker, Lambda, S3, Kinesis, Step Functions).
Qualification wording
Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field.
8+ years of experience in MLOps, ML infrastructure, or DevOps for AI/ML systems.
Deep knowledge of CI/CD and automation frameworks (GitHub Actions, Terraform, CloudFormation).
Hands-on experience with containerization (Docker) and orchestration (EKS).
Proficiency in Python and scripting for ML integrations.
Strong knowledge of cloud platforms (AWS preferred) and services relevant to ML (SageMaker, Lambda, S3, Kinesis, Step Functions).
Education & alternatives
We get excited about candidates, like you, because... - Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field. - 8+ years of experience in MLOps, ML infrastructure, or DevOps for AI/ML systems.
Tools in this posting
- Python
- AWS
- Databricks
- Docker
- Kubernetes
- MLflow
- S3
- SageMaker
- Terraform
- PyTorch
- TensorFlow
- scikit-learn
- Airflow
Source — Tool mentions in context
- Hands-on experience with containerization (Docker) and orchestration (EKS). - Proficiency in Python and scripting for ML integrations. - Strong knowledge of cloud platforms (AWS preferred) and services relevant to ML (SageMaker, Lambda, S3, Kinesis, Step Functions).
- Optimize ML workflows for performance, scalability, and cost-effectiveness across training and inference. - Leverage platforms such as AWS SageMaker, Databricks, Kinesis, Lambda, Kubernetes (EKS), and Docker for ML operations. - Collaborate with data engineering and software engineering teams to integrate ML services into large-scale distributed systems.
- Proficiency in Python and scripting for ML integrations. - Strong knowledge of cloud platforms (AWS preferred) and services relevant to ML (SageMaker, Lambda, S3, Kinesis, Step Functions). - Understanding of security, compliance, and governance in ML production systems.
- 8+ years of experience in MLOps, ML infrastructure, or DevOps for AI/ML systems. - MLflow, SageMaker, Databricks ML for experiment tracking, model registry, and lifecycle management. - Airflow, or Step Functions for workflow orchestration.
- Deep knowledge of CI/CD and automation frameworks (GitHub Actions, Terraform, CloudFormation). - Hands-on experience with containerization (Docker) and orchestration (EKS). - Proficiency in Python and scripting for ML integrations.
- Develop and manage CI/CD pipelines for ML to enable fast, reliable, and automated delivery of ML models. - Implement and manage model registry, experiment tracking, and versioning using tools like MLflow, SageMaker Model Registry, or equivalent. - Establish monitoring, observability, and alerting frameworks to detect drift, degradation, and anomalies in real-time.
- Airflow, or Step Functions for workflow orchestration. - MLFLow for monitoring ML models in production. - Deep knowledge of CI/CD and automation frameworks (GitHub Actions, Terraform, CloudFormation).
- MLFLow for monitoring ML models in production. - Deep knowledge of CI/CD and automation frameworks (GitHub Actions, Terraform, CloudFormation). - Hands-on experience with containerization (Docker) and orchestration (EKS).
How you’ll work with Data Science - Partner with ML Engineers to deploy and scale models built with frameworks like PyTorch, TensorFlow, and Scikit-learn. - Help data scientists track experiments, compare runs, and promote models to production.
- MLflow, SageMaker, Databricks ML for experiment tracking, model registry, and lifecycle management. - Airflow, or Step Functions for workflow orchestration. - MLFLow for monitoring ML models in production.
About Crunchyroll, LLC
We serve our community with humility, enabling joy and belonging for others.
In the employer’s words · Read in context
Job description
About Crunchyroll
Founded by fans, Crunchyroll delivers the art and culture of anime to a passionate community. We super-serve over 100 million anime and manga fans across 200+ countries and territories, and help them connect with the stories and characters they crave. Whether that experience is online or in-person, streaming video, theatrical, games, merchandise, events and more, it’s powered by the anime content we all love.
Join our team, and help us shape the future of anime!
About the role
Crunchyroll is growing and changing, presenting unique challenges and opportunities to support millions of anime fans around the world. The AI/ML team provides seamless help to our internal stakeholders, ensuring an exceptional experience for all Crunchyroll fans. The AI/ML team relies on strong MLOps practices to ensure models are reliable, scalable, and impactful in production.
- Design, build, and maintain end-to-end ML infrastructure and pipelines to support model training, deployment, and monitoring.
- Develop and manage CI/CD pipelines for ML to enable fast, reliable, and automated delivery of ML models.
- Implement and manage model registry, experiment tracking, and versioning using tools like MLflow, SageMaker Model Registry, or equivalent.
- Establish monitoring, observability, and alerting frameworks to detect drift, degradation, and anomalies in real-time.
- Partner with data scientists to productionize ML models, ensuring seamless transition from research to production.
- Optimize ML workflows for performance, scalability, and cost-effectiveness across training and inference.
- Leverage platforms such as AWS SageMaker, Databricks, Kinesis, Lambda, Kubernetes (EKS), and Docker for ML operations.
- Collaborate with data engineering and software engineering teams to integrate ML services into large-scale distributed systems.
- Drive best practices for MLOps, including reproducibility, governance, compliance, and security of deployed models.
How you’ll work with Data Science
- Partner with ML Engineers to deploy and scale models built with frameworks like PyTorch, TensorFlow, and Scikit-learn.
- Help data scientists track experiments, compare runs, and promote models to production.
- Translate research notebooks into production-grade pipelines with reproducible training and inference workflows.
- Co-own model lifecycle management: data, training, validation, deployment, monitoring, retraining.
- Ensure ML models align with software engineering best practices for testing, automation, and observability.
About You
We get excited about candidates, like you, because...
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field.
- 8+ years of experience in MLOps, ML infrastructure, or DevOps for AI/ML systems.
- MLflow, SageMaker, Databricks ML for experiment tracking, model registry, and lifecycle management.
- Airflow, or Step Functions for workflow orchestration.
- MLFLow for monitoring ML models in production.
- Deep knowledge of CI/CD and automation frameworks (GitHub Actions, Terraform, CloudFormation).
- Hands-on experience with containerization (Docker) and orchestration (EKS).
- Proficiency in Python and scripting for ML integrations.
- Strong knowledge of cloud platforms (AWS preferred) and services relevant to ML (SageMaker, Lambda, S3, Kinesis, Step Functions).
- Understanding of security, compliance, and governance in ML production systems.
- Excellent problem-solving and communication skills, with a proven ability to work with cross-functional teams of data scientists
About the Team
The R&D team is dedicated to developing, testing, and validating robust and scalable machine learning models that drive business objectives. Our focus includes enhancing operational processes through AI/ML solutions, such as trend analysis, anomaly detection, and the deployment of large language models (LLMs) for tasks like querying system health. Another major focus area is preserving, and improving customer experience and retention. We closely work with our stakeholders to ensure AI/ML objectives are clearly defined.
Why you will love working at Crunchyroll
In addition to getting to work with fun, passionate and inspired colleagues, you will also enjoy the following benefits and perks:
- Best-in class medical, dental, and vision private insurance healthcare coverage
- Access to counseling & mental health sessions 24/7 through our Employee Assistance Program (EAP)
- Free premium access to Crunchyroll
- Professional Development
- Company's Paid Parental Leave
- up to 26 weeks for birthing parents
- up to 12 weeks for non-birthing parents
- Hybrid Work Schedule
- Paid Time Off
- Flex Time Off
- 5 Yasumi Days
- Half-Day Fridays during the summer
- Winter Break
#LifeAtCrunchyroll ((select from the following job modalities for this role: #LI-Hybrid #LI-remote #LI-onsite))
About our Values
We want to be everything for someone rather than something for everyone and we do this by living and modeling our values in all that we do. We value
-
Courage. We believe that when we overcome fear, we enable our best selves.
-
Curiosity. We are curious, which is the gateway to empathy, inclusion, and understanding.
- Kaizen. We have a growth mindset committed to constant forward progress.
-
Service. We serve our community with humility, enabling joy and belonging for others.
Our commitment to diversity and inclusion
Our mission of helping people belong reflects our commitment to diversity & inclusion. It's just the way we do business.
We are an equal opportunity employer and value diversity at Crunchyroll. Pursuant to applicable law, we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Crunchyroll, LLC is an independently operated joint venture between US-based Sony Pictures Entertainment, and Japan's Aniplex, a subsidiary of Sony Music Entertainment (Japan) Inc., both subsidiaries of Tokyo-based Sony Group Corporation.
Questions about Crunchyroll’s hiring process? Please check out our Hiring FAQs: https://help.crunchyroll.com/hc/en-us/articles/360040471712-Crunchyroll-Hiring-FAQs
Please refer to our Candidate Privacy Policy for more information about how we process your personal information, and your data protection rights: https://tbcdn.talentbrew.com/company/22978/v1_0/docs/spe-jobs-privacy-policy-update-for-crpa-dec-21-22.pdf
Please beware of recent scams to online job seekers. Those applying to our job openings will only be contacted directly from @crunchyroll.com email account.
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Source & posting history
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Hyderabad, Telangana, India
- up to 12 weeks for non-birthing parents - Hybrid Work Schedule - Paid Time Off
More source context
- Winter Break #LifeAtCrunchyroll ((select from the following job modalities for this role: #LI-Hybrid #LI-remote #LI-onsite)) About our Values
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
- Status in our records
- Active
- First seen by us
- Jun 12, 2026
- Recorded sightings
- 68
- Last seen by us
- Oct 7, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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