Ai Ml Engineer

Job Summary:

Leadsquared is seeking a passionate and driven Machine Learning Engineer with 1-2 years of overall experience to join our innovative team in Bengaluru.

This role will focus on the development, deployment, and maintenance of machine learning models. While the primary focus is on ML, the ideal candidate will have a foundational understanding of data engineering principles and a willingness to work with data pipelines.

You will collaborate closely with data scientists and other engineers to translate business problems into scalable and impactful ML solutions.

Responsibilities:

• Participate in the entire machine learning lifecycle, from problem definition to deployment and monitoring.
• Develop and implement machine learning models using various algorithms and techniques.
• Experiment with different modeling approaches and evaluate their performance.
• Collaborate with data scientists to understand data requirements and feature engineering processes.
• Assist in the preparation and cleaning of data for model training and evaluation.
• Deploy and maintain machine learning models in production environments.
• Monitor model performance and retrain models as needed.
• Contribute to the development of MLOps pipelines and best practices.
• Work with any cloud platforms (e.g., AWS, Azure, GCP) and their ML services.
• Document machine learning models, experiments, and deployment processes.
• Stay up to date with the latest advancements in machine learning and related technologies.
• Collaborate with cross-functional teams to integrate ML solutions into existing products and services.
• Troubleshoot and resolve issues related to deployed ML models

Qualifications:

• Bachelor's degree in engineering.
• 1-3 years of overall experience with a focus on machine learning.
• Solid understanding of machine learning concepts, algorithms, and evaluation metrics.
• Hands-on experience with machine learning libraries and frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
• Proficiency in Python and relevant data science libraries (e.g., Pandas, NumPy).
• Basic understanding of data engineering principles and data pipelines.
• Familiarity with different data storage solutions (e.g., relational databases, data lakes).
• Exposure to cloud platforms (AWS, Azure, GCP) and their ML services (e.g., SageMaker, Azure ML, Vertex AI).
• Strong problem-solving and analytical skills.
• Good communication and collaboration skills.
• Ability to learn quickly and adapt to new technologies.