Machine Learning Engineer - W2

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Blocktxm Bench Recruitment: Streamlined Staffing Solutions Blocktxm provides robust bench recruitment services to help organizations access qualified talent with ease.

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About the opening:

We are looking for a skilled Machine Learning Engineer to design, develop, and deploy machine learning models that solve real-world problems. You will collaborate with data scientists, engineers, and business stakeholders to implement scalable AI/ML solutions.

Key Responsibilities:
• Design and develop machine learning models and algorithms for various use cases.
• Preprocess, clean, and analyze large datasets to extract meaningful insights.
• Train, tune, and optimize models for performance, accuracy, and scalability.
• Deploy machine learning models into production environments using MLOps best practices.
• Develop APIs or integrate models into applications for real-time or batch inference.
• Monitor model performance and retrain/update models as necessary.
• Work closely with software engineers, data scientists, and business teams to understand requirements and deliver AI-driven solutions.
• Stay updated with the latest ML research, tools, and techniques to improve existing models and processes.

Required Skills & Qualifications:
• Bachelor's or Master’s degree in Computer Science, Data Science, AI, or a related field.
• Strong experience with Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
• Hands-on experience with data preprocessing, feature engineering, and model development.
• Proficiency in SQL and NoSQL databases for data storage and retrieval.
• Experience with cloud platforms like AWS, Azure, or GCP for deploying models.
• Knowledge of containerization tools like Docker, Kubernetes.
• Familiarity with MLOps practices, CI/CD pipelines, and model monitoring tools.
• Strong problem-solving skills and the ability to work in a collaborative environment.

Preferred Qualifications:
• Experience with NLP, computer vision, or deep learning.
• Knowledge of big data technologies like Spark, Hadoop.
• Experience working with AutoML and model interpretability techniques.