AI Model Deployment Engineer

Role : MLOps Engineer Required Experience : 3 - 5 years

Job Location : : Gurugram

Job Description :

Must Have Skills : Cloud development (Capable),ML Ops - MLE

Key Responsibilities :

Collaborating with data scientists to understand model requirements, gather data, and refine models.

Responsible for the lifecycle of models, once deployed in production environments, through monitoring performance and model evolution.

Solve technical problems related to data processing, model building, and deployment.

Building and training machine learning models, including selecting datasets, performing statistical analysis, and refining models.

Deploy machine learning models to production environments, ensuring that models are integrated with software systems.

Monitor machine learning models in production, ensuring that models are performing as expected and that any errors or performance issues are identified and resolved quickly.

Required Skills :

Strong background in Data Science with hands-on ML experience and MLOps

Proficiency in Python and related libraries (Pandas, NumPy, Scikit-learn, etc.).

Ability to handle large-scale data and build end-to-end ML pipelines.

Hands on experience with MLflow, Kubeflow, Airflow, Docker, Kubernetes, SageMaker, Azure ML, Vertex AI.

Good understanding of statistical methods and data analysis.