MLOPS Engieer OR ML Engineer

MLOPS Engieer OR ML Engineer with DevOps


East Hanover, NJ


12 Months Contract

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: DataIKU, ModelDB, Kubeflow, Pachyderm, and Data Version Control (DVC) etc. Experience in Distributed computing, Data pipelines, and AI/Client.


3. Experience in DataIKU, Data Bricks, and Azure Kubernetes service.


4. Extensive experience with Unix/AIX/Linux environments


5. Experience with automation servers such as Jenkins, CloudBees, Travis, Gitlab actions


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


7. Familiarity with setting up Hyperparameter Tuning tools like DataIKU/ kubeflow/AWS Sagemaker or similar


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


9. Familiarity with JIRA and SNOW process.


Skills:


Preferred: DataIKU, Data Bricks, DevOps Tools like Jenkins, Gitlab


Good to have: Azure Kubernetes service, AWS Sagemaker/MLFlow

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. Suggest and implement best governance process and user access management


4. 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


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


6. Automate and streamline ML operations and processes.


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


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


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


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


11. 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


12. Consumer ticket triage and assignment(SNOW support)


13. Co-ordinate with required teams for ticket resolution, exploration and automation