Description

Core Responsibilities:

Cloud Platforms: Hands-on experience with AWS, GCP, or Azure.
ML Workflow Automation: Familiarity with tools like Kubeflow, Airflow, or MLflow.
Model Monitoring: Understanding of tools like Prometheus, Grafana, Seldon, or Evidently AI to track performance and drift.
Version Control: Solid Git experience for managing codebases and model versions.

Bonus Skills That Set You Apart:

* Infrastructure as Code (Terraform, CloudFormation, etc.)
* Feature stores (Feast) and data versioning (DVC)
* GenAI and LLM deployment experience
* Data engineering tools (Spark, Kafka)
* Serverless architectures and API-based ML model serving
* ML system security and data privacy best practices
* Domain expertise in NLP, computer vision, or other specialized ML fields
* Software engineering best practices (testing, code reviews)

Key Skills

Education

Any Graduate