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
Technical and Professional Requirements:
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 Masters degree in Computer Science Engineering or equivalent
Domain experience in Retail, CPG and Logistics etc.
Azure Certified DP100, AZ/AI900
Preferred Skills:
Technology->Data Science->Machine Learning
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Engineering - Software & QARole Category: Software DevelopmentRole: Data Platform EngineerEmployement Type: Full time