Artificial Intelligence/Machine Learning Engineer
2 weeks ago
Responsibilities :
1. Model Development and Implementation :
- Design, develop, and deploy machine learning models and algorithms tailored to solve specific business problems.
- Implement models into production environments ensuring scalability and efficiency.
2. Data Collection and Preprocessing :
- Gather, clean, and preprocess large datasets from various sources to ensure data quality and reliability.
- Perform feature engineering to enhance model accuracy.
3. Algorithm Selection and Optimization :
- Evaluate different machine learning algorithms and select the most appropriate one based on the problem at hand.
- Optimize algorithms for performance, accuracy, and speed.
4. Model Evaluation and Tuning :
- Regularly assess the performance of machine learning models using various evaluation metrics.
- Fine-tune models through techniques like hyperparameter tuning, cross-validation, and model ensembling.
5. Collaborative Development :
- Work closely with data scientists, software engineers, and domain experts to integrate machine learning models into existing systems or develop new applications.
- Collaborate with cross-functional teams to understand requirements and deliver solutions.
6. AI/ML Research :
- Stay updated with the latest advancements in AI and machine learning technologies.
- Apply state-of-the-art techniques to improve the performance and capabilities of existing models.
7. Tool and Framework Usage :
- Utilize various AI/ML tools and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) for model development and deployment.
- Ensure the use of best practices in coding and model development.
8. Documentation and Reporting :
- Maintain clear and concise documentation of models, algorithms, and processes.
- Prepare reports and presentations to communicate findings and results to stakeholders.
9. Ethical AI Practices :
- Ensure that AI/ML solutions are developed and deployed following ethical guidelines, addressing issues like bias, fairness, and transparency in machine learning models.
(ref:hirist.tech)-
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