Responsibilities:
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Develop, deploy, and maintain machine learning models using AWS Sagemaker and MLFlow.
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Implement end-to-end ML pipelines, from data ingestion to model deployment.
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Optimize model performance and scalability.
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Collaborate with data scientists to transition models from development to production.
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Implement Data Science Techniques (Statistical Analysis, Hypothesis Testing, Time Series Forecasting, Natural Language Processing, Classical Machine Learning Algorithms such as Linear Regression, Decision Trees, Random Forests, Support Vector Machines, K-Means Clustering)
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Monitor and manage deployed models to ensure performance and accuracy.
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Document processes and workflows to support reproducibility and transparency.
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Stay updated with the latest advancements in machine learning and related technologies.
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Design and implement data preprocessing and feature engineering pipelines.
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Integrate machine learning models with existing data systems and applications.
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Perform hyperparameter tuning and model validation to ensure high performance.
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Implement continuous integration and continuous deployment (CI/CD) pipelines for ML models.
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Conduct regular audits and performance checks of ML models in production.
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Provide technical guidance and mentorship to junior ML engineers and data scientists.
Technical Expertise Expected:
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Machine Learning Frameworks (TensorFlow, PyTorch & Scikit-learn, Keras, XGBoost, LightGBM, CatBoost)
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MLOps Tools (AWS Sagemaker, MLFlow, Kubeflow, Docker, Kubernetes, Azure Machine Learning, Google AI Platform, Jenkins and AWS/Azure/GCP CI/CD techniques)
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Programming Languages (Python, SQL, Scala, Java, R)
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ML Pipeline integration with Data Processing and Querying tech stack (Apache Spark, Apache Airflow, Apache Kafka, AWS Kinesis, Azure Data Factory, Pandas, Presto)
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Model Deployment (RESTful APIs, Flask, FastAPI, Sagemaker Deploy, AWS Lambda, Azure Functions, Tensorflow Serving, TorchServe, Kubernetes with KServe)
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Vers

📌 Machine Learning Engineer (Bengaluru)
🏢 TCP Corps
📍 Bengaluru