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  • Full Time
  • India
  • No. of Vacancies: 2
  • Experience: 5-7 Years

As a Senior Machine Learning Engineer, your role will involve leading and contributing to the development of complex machine learning solutions and models. You will be responsible for designing, implementing, and deploying advanced algorithms and systems that address challenging business problems. Your expertise in machine learning techniques, software engineering, and team leadership will be critical in driving innovation and ensuring the successful implementation of machine learning projects.

Key Responsibilities:

1. Machine Learning Model Development: Lead the end-to-end development of machine learning models, from data preprocessing and feature engineering to model selection and evaluation. Implement state-of-the-art algorithms and customize them as per specific project requirements.

2. Data Exploration and Analysis: Analyze and explore large datasets to extract meaningful insights and patterns. Collaborate with data engineers and data scientists to understand data sources, quality, and potential biases.

3. Model Training and Optimization: Develop and implement machine learning pipelines for training and fine-tuning models. Optimize hyperparameters, conduct cross-validation, and perform model performance analysis to improve model accuracy and efficiency.

4. Production Deployment: Work closely with DevOps and software engineering teams to deploy machine learning models into production environments. Ensure scalability, reliability, and real-time performance of deployed solutions.

5. Algorithm Selection and Research: Stay updated with the latest advancements in machine learning research and identify the most suitable algorithms for specific use cases. Explore novel techniques and experiment with cutting-edge models to drive innovation.

6. Code Review and Best Practices: Lead code reviews to ensure high-quality and maintainable code. Promote software engineering best practices, such as version control, automated testing, and documentation, within the machine learning team.

7. Performance Monitoring and Maintenance: Monitor the performance of deployed machine learning models and address any issues related to accuracy, data drift, or system integration. Develop monitoring and maintenance procedures to ensure ongoing model reliability.

8. Collaboration and Mentorship: Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to define project requirements and scope. Provide technical mentorship and guidance to junior machine learning engineers.

9. Research and Innovation: Conduct research and experiments to explore new machine learning techniques, frameworks, and tools. Innovate and develop custom solutions to address unique business challenges.

10. Communication and Presentation: Present machine learning solutions, research findings, and project progress to stakeholders, including senior management and non-technical teams. Clearly communicate complex technical concepts in a clear and understandable manner.