
ML Ops
1 week ago
6-8 years of over all experiencewith 3+ years in ML Opsengineering.
Strong proficiency in Python, with experience in machine learning librariessuch as TensorFlow, PyTorch, scikit-learn, etc.
Extensiveexperience with ML Ops frameworkslike DataIku (MUST Have),Kubeflow, MLflow, TensorFlow Extended (TFX), .
Strong experience in deploying and managing machine learning models on AWS environments
Proficiency in managing CI/CD pipelinesfor ML workflows using tools such as Jenkins, GitLab, CircleCI, etc.
Hands-on experience with containerization(Docker) and orchestration(Kubernetes) technologies for model deployment.
Mandatory skills : SageMaker, DataIku, Python, PySpark, AWS Services ; Good to have: AWS CDK
*Mandatory Competencies*
Data Science - Machine Learning (ML)
Python - Python
Beh - Communication
DevOps/Configuration Mgmt - DevOps/Configuration Mgmt - Jenkins
DevOps - Github
DevOps - Kubernetes
Big Data - Big Data - Pyspark
Cloud - AWS - AWS Lambda,AWS EventBridge, AWS Fargate
Data Science and Machine Learning - Data Science and Machine Learning - AI/ML
Data Science and Machine Learning - Data Science and Machine Learning - Python
DevOps/Configuration Mgmt - DevOps/Configuration Mgmt - GitLab,Github, Bitbucket
DevOps/Configuration Mgmt - DevOps/Configuration Mgmt - Containerization (Docker, Kubernetes).
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