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Specialist - DataOps

Job IDE3-C6-0A-EB-CD-B2
Location6 Locations Available
ProvinceAcross Canada
Date Posted2021-09-10
Posted Until2021-11-09
Job TypeFull-time
Job CategoryInformation Technology
The role of Specialist DataOps is to contribute to the whole solution by analyzing functional specifications to identify the best technical design (blueprint). The Specialist work with other DataOps Specialist, Data Scientists and Data Engineers across the company to accelerate the deployment and monitoring of models at scale.

The specialist also takes full responsibility of assigned deliverables, aligns own workload and focuses on key tasks in order to deliver as per service commitment, leveraging own expertise and skill set to achieve delivery goals. Depending on the assignment, the role may apply in either a Project, Enhancement or Support environment.

Main Responsibilities

Deliver – 25%

Implement best practices for ML model deployment and monitoring
Research and implement MLOPS tools, framework for Data Science and AI projects
Raise MLOPS maturity in the organization
Provide support to Data Scientist and Data Eng. from model design to deployment
Design and implement pipelines for continuous training, deployment and model monitoring in production
Design and improve end-to-end machine learning workflows that spans feature extraction, training, hyperparameter tuning and serving of production ML/AI models.
Collaborate with Data Scientist, Data Engineers and DataOps Specialists to improve automation, reproducibility, consistency and reduce friction of ML experimentation and improve testing and deployment strategies for ML/AI models

Technical Expertise – 25%

Excellent understanding of Data Science concepts with hands-on experience in ML model deployment in Azure and/or other cloud platform
Good knowledge of predictive, prescriptive, preventive and real-time model design, univariate and multivariate algorithms. Computer vision, Deep Learning, Ensemble model.
Create, review and maintain technical documentations.
Experience in implementing and deploying CI/CD pipelines using Azure pipelines.
Proficiency with Azure Cloud, Databricks, MLFlow and Azure ML.
Experience in operationalization of Machine Learning projects (MLOps) using at least one of the popular frameworks or platforms

Architecture – 25%

Contribute in developing the design and coding standards that will apply to the whole practice
Document blueprint based on requirements & functional designs
Participate in architectural discussions to ensure solutions are designed for successful deployment.
Involved in gathering, understanding and validating project specifications and participate in architecture design reviews

Quality Controls – 25%

Ensure Quality KPI are identified, measured and produced ensuring respect of development and deployment standards. Ensure right level of testing is consistent across all projects.
Identify problems, develop ideas and propose solutions within differing situations requiring analytical, evaluative or constructive thinking in daily work.
Perform reviews and quality checks after data has been loaded and model has been deployed.
Educate/mentor data scientists and teams on MLOPS best practices.
CredentialsAzure /DevOps Certification, DataBricks Certification
Knowledge of Hadoop ecosystem (Hive, Spark, HDFS, NiFi)
Essential SkillsFunctional competencies/Soft Skills

Strong communication skills, including the ability to speak clearly to technical and nontechnical people.
Self-driven, highly motivated, team player and able to learn quickly
Ability to learn quickly and to adapt to fast-paced environment
Technical skills/ Knowledge

Proficiency with ML Algorithms, Model deployment and drift detection.
Awareness of Agile principles (SAFE), automation
Experience with Azure (DataLake, DataFactory, DataBricks, Azure ML)
Knowledge of Big Data analytics technologies in a Cloud environment
Azure DevOps, Git, Python, PowerShell, Terraform, Azure ARM templates
Strong knowledge of Azure, SaaS, IaaS & PaaS Services.
Experience with AKS, Cognitive Services, MLOps, Data Factory
ExperienceMinimum 5-6 years overall work experience
5+ years experience of relevant technical expertise
OtherJob available in these locations:
Montreal, Quebec, Canada
Calgary, Alberta, Canada
Edmonton, Alberta, Canada
Ottawa, Ontario, Canada
Toronto, Ontario, Canada
Vancouver, British Columbia, Canada
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