Job
Description
Role & responsibilities Industry knowledge - Knows basics of machine learning, is aware of cloud services, Azure services, has a deep understanding of coding practices, knows how to guide teams on debugging the issues, can connect the dots to arrivie at a solution and is very good at presentation of the ideas, thoughts and solutions. Technical knowledge - has expertise in cloud technologies, specifically MS Azure, and services with hands on coding to Expertise in Object Oriented Python Programming with 4 -5 years experience. DevOps Working knowledge with implementation experience - 1 or 2 projects a minimum Hands-On MS Azure Cloud knowledge Understand and take requirements on Operationalization of ML Models from Data Scientist Help team with ML Pipelines from creation to execution List Azure services required for deployment, Azure Data bricks and Azure DevOps Setup Assist team to coding standards (flake8 etc) Guide team to debug on issues with pipeline failures Engage with Business / Stakeholders with status update on progress of development and issue fix Automation, Technology and Process Improvement for the deployed projects Setup Standards related to Coding, Pipelines and Documentation Adhere to KPI / SLA for Pipeline Run, Execution Research on new topics, services and enhancements in Cloud Technologies Responsible for successful delivery of MLOps solutions and services in client consulting environments; Define key business problems to be solved; formulate high level solution approaches and identify data to solve those problems, develop, analyze/draw conclusions and present to client. Assist clients with operationalization metrics to track performance of ML Models Agile trained to manage team effort and track through JIRA High Impact Communication - Assesses the target audience need, prepares and practices a logical flow, answers audience questions appropriately and sticks to timeline. Preferred candidate profile Education and Experience: Overall, 6 to 8 years of experience in Data driven software engineering with 3-5 years of experience designing, building and deploying enterprise AI or ML applications with at least 2 years of experience implementing full lifecycle ML automation using MLOps(scalable development to deployment of complex data science workflows) Bachelors or Master’s degree in Computer Science Engineering or equivalent Domain experience in Retail, CPG and Logistics etc. Azure Certified – DP100, AZ/AI900 Domain / Technical / Tools Knowledge : Object oriented programming, coding standards, architecture & design patterns, Config management, Package Management, Logging, documentation Experience in Test Driven Development and experience in using Pytest frameworks, git version control, Rest APIs Azure ML best practices in environment management, run time configurations (Azure ML & Databricks clusters), alerts. Experience designing and implementing ML Systems & pipelines, MLOps practices and tools such a MLFlow, Kubernetes, etc. Exposure to event driven orchestration, Online Model deployment Contribute towards establishing best practices in MLOps Systems development Proficiency with data analysis tools (e.g., SQL, R & Python) High level understanding of database concepts/reporting & Data Science concepts Hands on experience in working with client IT/Business teams in gathering business requirement and converting into requirement for development team Experience in managing client relationship and developing business cases for opportunities Azure AZ-900 Certification with Azure Architecture understanding is a plus
