Machine Learning Engineer

Job Description
 Model Development and Deployment :
 Develop end-to-end machine learning solutions, from data preprocessing to model deployment in production environments.
 Collaborate with cross-functional teams to understand business requirements and translate them into machine learning tasks.
 Data Exploration and Feature Engineering :
 Explore and analyze large datasets to identify patterns and trends.
 Engineer features and optimize data pipelines for efficient model training and inference.
 Algorithm Selection and Optimization :
 Choose appropriate machine learning algorithms based on project requirements.
 Optimize algorithms for performance, scalability, and robustness.
 Model Evaluation and Iteration :
 Evaluate model performance using appropriate metrics.
 Iteratively improve models based on feedback and changing business needs.
 Collaboration and Communication :
 Work closely with data scientists, software engineers, and domain experts to gather insights and domain knowledge.
 Effectively communicate technical concepts to non-technical stakeholders.
 Research and Innovation :
 Stay abreast of the latest advancements in machine learning and artificial intelligence.
 Apply state-of-the-art techniques and contribute to the continuous improvement of our machine learning capabilities.

Qualifications
 Bachelor's/Master's/Ph.D. degree in Computer Science, Machine Learning, Statistics, or related field.
 Years of hands-on experience in developing and deploying machine learning models.
 Strong programming skills in Python, with proficiency in libraries such as NumPy, Pandas, and scikit-learn.
 Experience with deep learning frameworks such as TensorFlow or PyTorch.
 Knowledge of cloud platforms (e.g., AWS, Azure, or Google Cloud) for model deployment.
 Strong understanding of data preprocessing, feature engineering, and model evaluation.
 Excellent problem-solving and analytical skills.

Preferred Skills
 Specialization in natural language processing, computer vision, or another relevant field.
 Previous experience with distributed computing and big data technologies.
 Familiarity with containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes).

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