We are seeking a talented and experienced Machine Learning Engineer to join our MLOps team. In this role, you will play a crucial part of all Machine Learning lifecycle opeations, offline and online feature serving for AI models, Data pipeline for ML, Model train, CI\CD for AI models and more.

As a key member of the MLOps team, you will collaborate with cross-functional teams to streamline the end-to-end machine learning lifecycle and contribute to the optimization of our MLOps processes.

Responsibilities

• Data pipeline for ML: Design, develop, and implement scalable and reliable systems for feature engineering that can serve both online and offline inference flows.
• Model Deployment: Design, develop, and implement scalable and reliable systems for deploying machine learning models into production.
• Implement CI/CD pipelines for seamless model updates and releases.
• Monitoring and Quality: Establish robust monitoring and logging mechanisms for deployed models to identify anomalies and drifts in AI components.
• Collaborate with DevOps and DatOps teams to integrate machine learning solutions into existing infrastructure.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field.
• Proven experience as a Machine Learning Engineer, with a focus on model deployment and feature pipeline.
• Strong programming skills in languages such as Python, Java, or Scala.
• Experience with containerization technologies (Docker, Kubernetes) and cloud platforms (AWS, Azure, GCP).
• Familiarity with MLOps tools and frameworks (e.g., MLflow, Kubeflow, Sagemaker).
• Understanding of continuous integration and continuous deployment (CI/CD) practices.
• Excellent communication skills.