Responsible for the ML Ops pipelines creation and automation on cloud and premise, CI/CD pipelines orchestration across multiple projects
Responsible for designing and reviewing ML architectural designs with stakholders.
ML models code refactoring, training, retraining, deployment, testing and continuous monitoring for drift.
Applying software engineering rigor and best practices to machine learning, including CI/CD, automation, etc
Optimisation of model hyper parameters
Requirements
10+ years of demonstrated expertise in building ML/automation pipelines from scratch (Including model versioning, model and data lineage, monitoring, model hosting and deployment, model optimization, scalability, orchestration, continuous learning, Automated pipelines)
Knowledge of architectural design and implementation of end-to-end ML solutions.
Should be proficient in AWS components such as Sage maker, AWS Lambda, other AWS services, serverless services, etc.
Strong knowledge of python and PySpark. Working knowledge of R is preferred
Strong knowledge of working tools like Docker, Kubernetes, Jenkins
Bachelor’s or Master’s in Computer Applications/Computer Science OR Specialization/courses in ML/DS, having deep understanding of SDLC.