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21/06 Himani
HR at Matrix Management Services

Views:67 Applications:13 Rec. Actions:Recruiter Actions:2

Data Science Role - Machine Learning (8-14 yrs)

Anywhere in India/Multiple Locations Job Code: 1113732

Job Description:

1. 8 to 10 years of industry experience working in Software Engineering, DevOps or Data Engineering with Data Science and MLOps experience.

2. Strong DevOps, Data Engineering and Client background with AWS Experience with one or more of MLOps tools: ModelDB, Kubeflow, Pachyderm, and Data Version Control (DVC) etc. Experience in Distributed computing, Data pipelines, and AI/Client.

3. Review and influence the engineering design, architecture and technology stack across multiple products

4. Experience in Azure ML, Data Bricks, and Azure Kubernetes service.

5. Extensive experience with Unix/AIX/Linux environments

6. Experience with automation servers such as Jenkins, CloudBees, Travis, GitHub actions

7. Experience with logging tools such as Splunk, ElasticSearch, Kibana, Logstash

8. Familiarity with setting up Hyperparameter Tuning tools like optuna/ kubeflow/AWS Sagemaker or similar

9. Familiarity with setting up model and experiment Versioning technologies like MLFLow/Kubeflow/AWS Sagemaker or similar

Roles & Responsibilities:

1. Ensure reliability and cost saving. Scale the proof of concept product to enterprise grade application with all the required components for reliability, scalability, monitoring and security.

2. Suggest and implement the best practices from Software engineering to ML workflow to ensure CI/CD, reproducibility and quick delivery cycle.

3. Lead and drive the deployment of ML models, life cycle management and monitoring of Machine Learning(ML) and Deep Learning (DL) models in in all stages leading to production

4. Be a subject matter expert on DevOps practices, CI/CD and Configuration Management with assigned engineering team

5. Automate and streamline ML operations and processes.

6. Build and maintain tools for deployment, monitoring, and operations. Also troubleshoot and resolve issues in development, testing, and production environments

7. Operate and maintain systems supporting the provisioning of new clients, applications, and features

8. Work to improve Data scientist and Data engineer productivity and delivery speed by enabling them to be more self-sufficient with automated operational processes.

9. Successfully devise and implement strategies to ensure ML heavy systems operate with high accuracy in Production and adapt to discovered needs.

10. Collaborate with Data Scientists, Data Engineers, ML Engineers, cloud platform and application engineers to create and implement cloud policies and governance for ML/DL model life cycle

Women-friendly workplace:

Maternity and Paternity Benefits

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