Description

Key Responsibilities

• Develop, train, test, and implement machine learning and AI models using relevant datasets to address business and project requirements.
• Build and maintain automated machine learning pipelines for model development, deployment, and ongoing operations.
• Perform data preprocessing, feature engineering, feature selection, and model evaluation to ensure data quality and model performance.
• Apply machine learning techniques, including natural language processing (NLP), computer vision, and predictive analytics, based on project needs.
• Deploy machine learning models and AI-driven applications into production environments.
• Monitor deployed models for performance, reliability, and accuracy, and troubleshoot issues as required.
• Implement MLOps practices to streamline model lifecycle management, deployment automation, and production maintenance.
• Work with data scientists, data engineers, software engineers, and other technical team members to integrate AI/ML solutions into existing workflows.
• Document model development processes, deployment procedures, methodologies, and technical findings.
• Identify opportunities to improve ML workflows, operational efficiency, and automation.

Required Qualifications

• 6 years of relevant experience in machine learning engineering, MLOps, AI engineering, or a related field.
• Hands-on experience developing, training, evaluating, and deploying machine learning models.
• Understanding of MLOps principles, including model lifecycle management, deployment, monitoring, and maintenance.
• Experience with data preprocessing, feature engineering, model validation, and performance evaluation.
• Proficiency in Python and familiarity with commonly used machine learning libraries and frameworks.
• Experience working with data pipelines and integrating machine learning models into production applications.
• Strong analytical, problem-solving, documentation, and collaboration skills.

Qualifications

• Experience with Databricks for data processing, machine learning workflows, or model development.
• Experience with Amazon Web Services (AWS) and cloud-based ML deployment.
• Familiarity with MLflow or similar tools for experiment tracking and model lifecycle management.
• Knowledge of automated deployment pipelines, CI/CD, model monitoring, and production troubleshooting.
• Exposure to NLP, computer vision, predictive analytics, or other applied AI techniques

Key Skills

Education

Bachelor's degree