Job Description – (For resourcer to read carefully before start working on the role)We are looking for a highly experienced Senior Machine Learning Engineer to design, develop, and deploy production-grade ML solutions at scale. As a key member of our AI Engineering team, you will work alongside data engineers, scientists, and platform teams to drive impactful AI initiatives across the organization.You will be responsible for building scalable ML pipelines, developing models using advanced machine learning and deep learning techniques, and ensuring successful deployment and performance in cloud environments. Key Responsibilities:

• Design, develop, and train machine learning and deep learning models tailored to business and product needs.
• Prepare and preprocess structured and unstructured datasets using robust and scalable techniques.
• Deploy ML models into production cloud environments (Azure, GCP, or AWS).
• Perform hyperparameter tuning and model optimization to improve accuracy and performance.
• Collaborate with data engineers to build end-to-end ML pipelines that are maintainable and production-ready.
• Monitor and troubleshoot deployed models to ensure sustained performance.
• Stay up to date with the latest research and tools in ML, DL, and cloud-native AI services.

REQUIRED SKILLS AND QUALIFICATIONS:

• 8+ years of experience in machine learning engineering or related fields.
• Strong foundation in machine learning algorithms and deep learning architectures (e.g., CNNs, RNNs, Transformers).
• Proficiency in Python and commonly used ML/DL libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience working with cloud-based ML platforms (Azure ML, GCP AI Platform, AWS SageMaker).
• Expertise in model deployment, including containerization (e.g., Docker) and serving frameworks.
• Solid understanding of data preprocessing techniques, feature engineering, and pipeline orchestration.
• Skilled in model evaluation, optimization, and hyperparameter tuning techniques (e.g., Grid Search, Bayesian Optimization).

PREFERRED QUALIFICATIONS:

• Experience with MLOps tools and practices (e.g., MLflow, Kubeflow, CI/CD for ML).
• Exposure to GenAI or LLM-based models is a plus.
• Master’s or PhD in Computer Science, Data Science, or a related field.
  Job Type: Full-time
  Pay: $21.76-$67.54 per hour

Benefits:

• Work from home

Application question(s):

• do you have 8+ years of ML engineering experience?
• Have you developed and deployed ML/DL models in Azure, AWS, or GCP?
• Have you worked with GenAI or LLM models?

Experience:

• Machine Learning Engineer: 7 years (required)

• Design, develop, and train machine learning and deep learning models tailored to business and product needs.
• Prepare and preprocess structured and unstructured datasets using robust and scalable techniques.
• Deploy ML models into production cloud environments (Azure, GCP, or AWS).
• Perform hyperparameter tuning and model optimization to improve accuracy and performance.
• Collaborate with data engineers to build end-to-end ML pipelines that are maintainable and production-ready.
• Monitor and troubleshoot deployed models to ensure sustained performance.
• Stay up to date with the latest research and tools in ML, DL, and cloud-native AI services