ML Ops Engineer

Role & Responsibilities :

Good knowledge of Azure and Databricks cloud platform and services.

Strong hands-on experience in end-to-end ML solution orchestration for – 1) Data / Feature Engineering, 2) Model Training / Inferences, 3) Real-time / Batch Processing.

Hands-on experience in backend development using FastAPI.

Very good knowledge of MLOps concepts like data drift, model drift, online learning.

Very good hands-on experience working with Python, PySpark, T-SQL, CI / CD on Github Actions.

Collaborate with data engineers, data scientists and other stakeholders.

At least 3 to 5 years of relevant experience in MLOps engineering role.

Good to have knowledge in deployment of LLM based GenAI solutions.