Data Driven AI Engineer

2 weeks ago

Pune, Maharashtra, India beBeeMlOps Full time ₹ 1,50,00,000 - ₹ 2,20,00,000

As a Machine Learning Operations Engineer, you will be responsible for building and maintaining data pipelines to process large datasets.

You will develop and implement machine learning models for various applications including classification, regression, clustering, and recommendations.

The successful candidate will engineer features and optimize model performance through hyperparameter tuning and validation.

Achieving the best possible results requires automating model building, training, and deployment by creating CI/CD/CT Pipelines.

It is essential to stay updated on new tools and techniques in machine learning and AI.

Key Responsibilities:

• Prepare, clean, and process large datasets; build and maintain data pipelines.

• Develop and implement machine learning models for various applications (classification, regression, clustering, recommendations, etc.).

• Engineer features and optimize model performance through hyper parameter tuning and validation.

• Automating model building, training and deployment by building CI/CD/CT Pipelines

• Stay updated on new tools and techniques in machine learning and AI.

MLOps Engineer: Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Statistics, or related field.

• 3-5 years of experience in AI/ML engineering and Python with a focus on building production-ready models.

• Understanding of machine learning algorithms, deep learning architectures, natural language processing (NLP), and information retrieval techniques.

• Proven ability to deploy and manage AI/ML solutions on major cloud platforms.

• Experience with MLOps principles and tools for model deployment, monitoring, and lifecycle management.

• Familiarity with containerization using Docker and Kubernetes, OpenShift, and MLOps frameworks like Kubeflow, MLFlow, DataRobot, and Airflow.

What You'll Need

• A bachelor's or master's degree in a relevant field such as computer science, engineering, mathematics, or statistics.

• At least three to five years of hands-on experience in AI/ML engineering with a focus on building production-ready models.

• Proficiency in Python and a strong understanding of machine learning algorithms, deep learning architectures, NLP, and information retrieval techniques.

• The ability to work with large datasets, build and maintain data pipelines, and automate model building, training, and deployment.

• Experience working with containerization technologies like Docker and Kubernetes, as well as MLOps frameworks and tools.

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