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  • Full Time
  • India
  • No. of Vacancies: 2
  • Experience: 3-5 Years

As an ML Engineer, your role will revolve around designing, developing, and implementing machine learning (ML) models and systems. You will work closely with data scientists, software engineers, and other stakeholders to create robust and scalable ML solutions that address business challenges. Your expertise in machine learning algorithms and software engineering will play a critical role in transforming data into actionable insights and driving innovation within the organization.

Key Responsibilities:

1. ML Model Development: Collaborate with data scientists and domain experts to design and develop ML models that solve complex business problems. This involves understanding the problem statement, selecting appropriate algorithms, and optimizing model performance.

2. Data Preparation and Analysis: Clean, preprocess, and analyze large datasets to extract meaningful features and insights. Ensure data quality and develop data pipelines that facilitate efficient model training and evaluation.

3. Model Training and Evaluation: Implement and execute machine learning experiments using frameworks such as TensorFlow, PyTorch, or scikit-learn. Train models on large-scale datasets, fine-tune hyperparameters, and evaluate model performance using appropriate metrics.

4. Algorithm Selection and Optimization: Identify the most suitable machine learning algorithms and techniques for a given problem. Optimize and fine-tune models to improve accuracy, efficiency, and scalability.

5. Deployment and Integration: Collaborate with software engineers to deploy ML models into production environments. Integrate ML solutions with existing systems, APIs, and databases to enable real-time predictions and decision-making.

6. Performance Monitoring and Improvement: Monitor the performance of deployed ML models and systems, identifying areas for improvement and implementing enhancements as needed. Continuously optimize models to adapt to changing data distributions and evolving business requirements.

7. Model Interpretability and Explainability: Develop techniques to interpret and explain the decisions made by ML models. Ensure transparency and accountability in model outputs, especially in regulated or sensitive domains.

8. Collaboration and Documentation: Work closely with cross-functional teams to understand business requirements and translate them into ML solutions. Document code, processes, and model architectures to facilitate knowledge sharing and maintainability.

9. Research and Innovation: Stay updated with the latest advancements in the field of machine learning and data science. Explore new techniques, algorithms, and tools to drive innovation and improve the organization’s ML capabilities.

10. Performance Optimization: Optimize ML models and algorithms for performance, memory usage, and scalability. Explore techniques such as distributed computing, parallel processing, and model compression to enhance efficiency.