What you'll do:

• End-to-End ML Lifecycle Management
• Design and implement end-to-end Machine Learning (ML) pipelines, from data preparation to model deployment and monitoring.
• Collaborate with data scientists and engineers to streamline the ML workflow and ensure scalability.
• Deploy ML models into production environments and monitor their performance.
• Implement automated monitoring and alerting systems to detect and respond to model drift or degradation.
• Work closely with DevOps teams to manage infrastructure for ML workloads, ensuring scalability, reliability, and security.
• Implement containerization and orchestration solutions for ML applications.
• Collaborate with cross-functional teams, including data scientists, software engineers, and business stakeholders.
• Document MLOps processes, best practices, and guidelines.

What makes you a great fit:

• Experience required 4-6 years in relevant field.
• 2+ years of relevant experience in MLOps
• Proficient in programming languages such as Python and experience with ML frameworks
• Hands-on experience with data engineering tools and frameworks (e.g., Apache Spark, Hadoop, Airflow).
• Knowledge of containerization (Docker) and orchestration (Kubernetes) technologies.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and their ML services.
• Familiarity with infrastructure as code (IaC) tools.
• Strong communication and collaboration skills, with the ability to work effectively in a cross-functional team.
• Proven ability to troubleshoot complex issues and find practical solutions.