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Data Science MLOPS

3 - 7 years

0 Lacs

haryana

Posted:1 week ago| Platform: Shine logo

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Skills Required

python numpy airflow etl docker kubernetes aws gcp azure git pandas scikitlearn pytorch tensorflow ml ops mlflow kubeflow sagemaker vertex ai azure ml cicd

Work Mode

On-site

Job Type

Full Time

Job Description

As a Machine Learning Engineer, your primary responsibility will be to develop, train, and validate predictive and analytical models using machine learning techniques. You will collaborate closely with data engineers and business teams to define data requirements and success metrics. In addition, you will deploy machine learning models into production following ML Ops best practices and build automated pipelines for model training, testing, monitoring, and retraining. Your role will also involve optimizing model performance, ensuring scalability and reliability, monitoring model drift and performance degradation, and continuously improving the models. Furthermore, you will implement CI/CD practices for machine learning workflows. To excel in this role, you must possess a strong proficiency in Python, including libraries such as Pandas, NumPy, Scikit-learn, and PyTorch/TensorFlow. Experience with ML Ops tools/frameworks like MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, or Azure ML is essential. A good understanding of data engineering concepts such as ETL, data pipelines, and APIs is required, along with hands-on experience in containerization and orchestration using Docker and Kubernetes. Knowledge of cloud platforms like AWS, GCP, or Azure for ML model deployment is also crucial. Your background should include a strong foundation in statistics, machine learning algorithms, and model evaluation techniques, along with familiarity with version control using Git and CI/CD pipelines. Ideally, you should hold a Bachelors or Masters degree in Computer Science, Data Science, Statistics, or a related field. Previous experience in handling large-scale data pipelines and real-time model serving will be advantageous. Exposure to feature stores and automated model monitoring is a plus. Strong problem-solving, analytical, and communication skills are highly desirable for this role.,

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