Brillio 21mo ago

Senior AI/ML Engineer - R01563569

IndiaEmployeesenior

Data ScienceOtherData EngineeringMachine Learning EngineerData

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Overview

Senior AI/ML Engineer Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML),…

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azurekubernetespythonpytorchsparktensorflowci-cdforecastingmachine-learningstatistical-modeling

Senior AI/ML Engineer

Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio

Data Science Advanced: Data Specialist

Key Responsibilities • Design, implement, and manage scalable machine learning (ML) pipelines using Azure ML, Databricks, and PySpark. • Build and maintain automated CI/CD pipelines with Github and Github Action, incorporating SonarQube to ensure code quality and security standards. • Utilize Azure Kubernetes Service (AKS) to containerize and deploy machine learning models, ensuring high availability and scalability. • Have understanding of over all architecture and can work on scalable solutions • Develop reusable templates for various ML use cases to streamline the model deployment process and enhance operational efficiency. • Design and manage APIs to facilitate seamless interaction between ML models and other applications, ensuring robust, secure, and scalable API interfaces. • Perform model optimization, monitor data drift, data refresh checks, and ensure the ML pipelines are cost-efficient. • Implement cost monitoring and management strategies to ensure efficient use of resources, particularly for model training and deployment phases. • Work closely with data scientists, DevOps, and IT teams to deploy and manage machine learning models across environments. • Provide thorough documentation for ML workflows, pipeline templates, and optimization strategies to support cross-team collaboration.