Saarthee - Junior Machine Learning Engineer - Python/R (3-6 yrs)

Responsibilities :

- Design and develop machine learning models for various applications, such as recommendation systems, sentiment analysis, and content personalization.

- Deploy machine learning solutions to meet enterprise goals and support experimentation and innovation.

- Ensure that enterprise infrastructure and data pipelines are well-equipped to support machine learning solutions.

- Implement and optimize machine learning algorithms to improve model performance and accuracy.

- Collaborate with data scientists, engineers, and stakeholders to understand business requirements and translate them into effective ML solutions.

- Design and implement scalable machine learning pipelines for data preprocessing, model training, and deployment.

- Monitor and analyze model performance, identify opportunities for improvement, and implement enhancements to ensure optimal results.

- Research and stay up to date with the latest advancements in machine learning and AI technologies and apply them to the organization's projects.

- Mentor and guide junior machine learning engineers, promoting best practices and fostering a culture of continuous learning and improvement.

- Understand and translate business and functional needs into machine learning problem statements.

- Translate complex machine learning problem statements into specific deliverables and requirements.

- Design and develop scalable solutions that leverage machine learning and deep learning models to meet enterprise requirements.

- Work closely with UX designers and product teams to improve the product user experience.

Qualifications :

- Bachelor's or master's degree in computer science, Engineering, Mathematics, or a related field.

- Minimum of 3-6 years of experience in machine learning, with a proven track record of designing, developing, and deploying ML models in a production environment.

- Strong problem-solving skills and understanding of machine learning algorithms, including classification, regression, clustering, and deep learning.

- Proficiency in at least one programming language commonly used in machine learning, such as Python, R, and Javascript.

- Knowledge of popular machine learning frameworks and libraries, such as TensorFlow, PyTorch, or scikit-learn.

- Familiarity with cloud-based machine learning platforms, like AWS SageMaker, Google Cloud AI, or Azure Machine Learning.

- Expertise in data structures, algorithms, and software design patterns.

- Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.