Machine Learning Specialist

About The Role

This role involves operationalizing machine learning models and AI functionalities into robust, scalable, and secure production systems that support paid risk intelligence offerings.

Key Responsibilities

• Create and maintain scalable pipelines for model training, deployment, and inference.
• Deploy machine learning models in production environments leveraging continuous integration and deployment (CI/CD), containerization, and cloud platforms.
• Ensure that deployed models satisfy necessary requirements related to performance, latency, and reliability.
• Implement lifecycle management including model versioning, monitoring, and automated retraining processes.
• Identify and counteract issues such as model drift, data distribution changes, and performance degradation.
• Define and uphold operational benchmarks and standards for AI systems.
• Collaborate closely with data scientists to operationalize and scale machine learning models.
• Partner with product and engineering teams to integrate machine learning features in customer-facing solutions.
• Convert experimental machine learning developments into stable production assets.
• Guarantee compliance of machine learning systems with privacy, security, and regulatory mandates.
• Advocate and apply principles of explainability, auditability, and ethical AI use.
• Provide clear documentation on models and operational decision-making processes.

Required Skills and Knowledge

• Over 8 years of experience in machine learning engineering, MLOps, or general software engineering roles.
• Proficiency in Python programming and working within cloud environments.
• Practical experience using machine learning platforms such as MLflow, Kubeflow, SageMaker, and container orchestration technologies like Kubernetes.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a closely related discipline.
• Master’s degree in Big Data, Artificial Intelligence, or an equivalent field.