Tech Stack

Description

You will deploy, maintain, and monitor AI/ML systems powering our platform, working closely with data scientists and product teams to ensure scalable, reliable, production-grade AI solutions. This includes operationalizing large language models (LLMs) and building robust monitoring, logging, and alerting systems.

Requirements

• 5+ years of experience as a Machine Learning Engineer or MLOps Engineer
• Proven experience deploying and maintaining ML models in production at scale
• Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar)
• Strong proficiency in Python; familiarity with PyTorch or TensorFlow
• Deep knowledge of Docker and Kubernetes for production ML systems

Responsibilities

• Design, implement, and maintain ML deployment pipelines for scalable production systems
• Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability
• Build robust model monitoring, logging, and alerting systems to track performance and detect drift
• Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment
• Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems