Cloud ml platforms like databricks or aws sagemaker
Strong software engineering background for large scale systems
The role involves owning the full machine learning lifecycle including infrastructure, deployment, monitoring, retraining, and governance within a governed environment
Job Summary
The role involves owning the full machine learning lifecycle including infrastructure, deployment, monitoring, retraining, and governance within a governed environment.
Candidates must bridge the gap between experimentation and production to enable faster, safer, and reproducible delivery of ML models.
The position requires balancing experimentation velocity with operational reliability while adhering to strict risk management and governance expectations.
Matching Summary
Match Score: 75
The role involves owning the full machine learning lifecycle including infrastructure, deployment, monitoring, retraining, and governance within a governed environment.
Skills & Requirements
Must-have
Deploying machine learning models in production
Cloud ML platforms like Databricks or AWS SageMaker
Strong software engineering background for large scale systems
Proficiency with Docker and CI/CD tooling
Understanding of distributed systems and API services
Nice-to-have
Experience with MLflow feature stores and model registry
Hands on data validation drift detection and observability
Infrastructure as Code using Terraform or CloudFormation
Experience in financial services or regulated industries
Responsible deployment of Generative AI systems
Key Requirements
Proven track record of deploying ML models in production
Experience with cloud ML platforms such as Databricks or AWS SageMaker
Strong practical understanding of machine learning algorithms and statistical methods