Experience in building GenAI based complex workflow in auto-pilot or co-pilot mode which run in production
Experience in building Responsible AI guardrails and metrics around traditional AI or Gen AI models
Familiar with latest Gen AI, Agent based products and AI coding tools in the market
Fundamental understanding of NLP, transformer and embedding space
Design and implement Machine learning models and data ingestion pipelines
Experience in building software products used in production
Create and maintain optimal data pipeline architecture by assembling large, complex data sets to meet functional and non-functional business requirements
Identify and implement internal process improvements including automating manual processes, optimizing data delivery, and redesigning infrastructure for greater scalability
Support the building of machine learning, data platforms, and infrastructure required for optimal data extraction, transformations, and loading of data from a wide variety of data sources
Work with architecture, data, and design teams to assist with data related technical issues and support data infrastructure needs
Deploy ML models in AWS environment specifically in AWS Sage Maker environment
Implement Model Monitoring, Data Quality Checks, Data Drifts in Inference Pipelines
Support ML teams in the delivery of continuous integration, continuous deployment, providing templates and patterns
Perform root cause analysis for production issues where the root cause is in infrastructure, environment, configuration, or deployment routines; understand when to escalate to product development teams; remediate root causes and implement preventative actions
Own the AWS stack which comprises all ML resources and collaborate on managing ML infrastructure costs
Establish standards and practices around MLOps, including governance, compliance, and data security
Uses customer management system to provide status on open customer issues and properly verifies when an issue can be closed

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