AI Model Deployment Engineer

Location - Noida UP / Gurugram HR / Pune MH (3 days hybrid / week)

Experience : 6-12 years

Shift Timing : 1 : 00 PM to 10 : 00 PM

Major Skills : ML OPS, ML Ops frameworks like Kubeflow , MLflow , TensorFlow Extended (TFX) , KubeFlow Pipelines, AWS Cloud, CI / CD pipeline, containerization (Docker) and orchestration (Kubernetes)

Job Description

• 6+ years of experience in ML Ops engineering or a related field.
• Strong proficiency in Python, with experience in machine learning libraries such as TensorFlow, PyTorch, scikit-learn, etc.
• Extensive experience with ML Ops frameworks like Kubeflow, MLflow, TensorFlow Extended (TFX), KubeFlow Pipelines, or similar.
• Strong experience in deploying and managing machine learning models in cloud environments (AWS, GCP, Azure).
• Proficiency in managing CI / CD pipelines for ML workflows using tools such as Jenkins, GitLab, CircleCI, etc.
• Hands-on experience with containerization (Docker) and orchestration (Kubernetes) technologies for model deployment.
• Skills : SageMaker, PySpark, AWS Services ;

Nice to have : MLOps

Job In

Experience Required (Min) - 6

Experience Required (Max) - 8

Primary Skill - Data Science and Machine Learning - Data Science and Machine Learning - AI / ML

Employee Class - Permanent