MLOps Engineer

LenDenClub

3 - 5 years

2 - 6 Lacs

mumbai

Posted:10 hours ago| Platform:

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Skills Required

continuous integration kubernetes data validation production configuration ci/cd docker ansible salesforce git java devops linux jenkins shell scripting jira cd python github maven microsoft azure engineering continuous delivery troubleshooting aws

Work Mode

Work from Office

Job Type

Full Time

Job Description

LenDenClub is looking for MLOps Engineer to join our dynamic team and embark on a rewarding career journey

"Model Deployment and Orchestration:Design and implement automated pipelines for deploying ML models into production environments, leveraging containerization technologies (e
g
, Docker, Kubernetes) and orchestration tools (e
g
, Kubeflow, MLflow)
Develop scripts and configuration files for model packaging, versioning, and deployment, ensuring consistency and reproducibility across different environments
Infrastructure Provisioning and Management:Collaborate with DevOps and cloud engineers to provision and configure infrastructure resources (e
g
, virtual machines, Kubernetes clusters) for hosting ML workloads
Optimize infrastructure setup for scalability, performance, and cost efficiency, considering factors such as workload demand, data volume, and computational requirements
Continuous Integration and Delivery (CI/CD):Implement CI/CD pipelines for automating the testing, validation, and deployment of ML models, integrating with version control systems (e
g
, Git) and CI/CD platforms (e
g
, Jenkins, GitLab CI)
Monitor pipeline execution, track build artifacts, and manage deployment releases to ensure smooth and reliable model updates
Monitoring and Alerting:Set up monitoring and alerting systems to track the performance, health, and usage metrics of deployed ML models in real-time
Implement logging, telemetry, and observability solutions to capture model predictions, input data distributions, and system anomalies for troubleshooting and analysis
Security and Compliance:Implement security best practices and access controls to protect sensitive data and ensure regulatory compliance (e
g
, GDPR, HIPAA) in ML workflows
Perform security assessments, vulnerability scans, and audits of MLOps infrastructure and applications to identify and remediate security risks
"

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