Machine Learning Engineer

*Only W2 profile*

We are seeking a highly skilled Machine Learning Engineer to design, develop, deploy, and optimize scalable machine learning solutions. The ideal candidate will have strong experience in supervised and unsupervised learning, deep learning, NLP, MLOps, and Azure cloud technologies. You will collaborate with data scientists, software engineers, and business stakeholders to build production-ready AI/ML applications.

Key Responsibilities

• Design, develop, and deploy machine learning models for production environments.
• Build predictive models using supervised and unsupervised learning techniques.
• Develop deep learning models using neural networks for complex business problems.
• Implement NLP solutions for text analytics, classification, sentiment analysis, chatbots, or LLM-based applications.
• Train, evaluate, tune, and optimize machine learning models for performance and scalability.
• Build and maintain ML pipelines using MLOps best practices.
• Deploy and monitor ML models on Microsoft Azure cloud.
• Work with large datasets using SQL and Python.
• Collaborate with cross-functional teams to integrate ML models into production applications.
• Automate model training, deployment, monitoring, and retraining processes.
• Ensure model governance, reproducibility, and version control using DevOps/MLOps tools.

Required Qualifications

• Bachelor''s or Master''s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
• 5+ years of experience in Machine Learning Engineering or AI development.
• Strong experience with supervised and unsupervised learning algorithms.
• Hands-on experience with neural networks and deep learning.
• Experience implementing NLP solutions.
• Strong programming skills in:
  • Python
  • R
  • SQL
• Experience with one or more ML frameworks:
  • TensorFlow
  • Keras
  • PyTorch
• Experience deploying ML models on Microsoft Azure.
• Knowledge of Azure Machine Learning services.
• Experience with DevOps/MLOps practices including CI/CD for ML pipelines.
• Familiarity with Git, Docker, Kubernetes, and model monitoring.
• Strong analytical and problem-solving skills.

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