MLOps Engineer

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.