As an MLOps Engineer, you bridge the gap between data science and operations by automating, monitoring, and scaling ML model lifecycles—from training to production—ensuring efficiency, reproducibility, and robust model performance.

Key Responsibilities:

• Design, build, and maintain scalable MLOps pipelines for continuous integration and deployment (CI/CD) of machine learning models.

• Automate the end-to-end ML lifecycle including data ingestion, model training, validation, and deployment.

• Collaborate with data scientists to productionize models and ensure reproducibility across environments.

• Monitor model performance and implement tools for drift detection, logging, versioning, and rollback strategies.

• Implement containerization and orchestration using Docker and Kubernetes for model deployment at scale.

• Manage model serving infrastructure (e.g., TensorFlow Serving, TorchServe, or custom APIs).

• Ensure governance and compliance through secure access, audit trails, and environment controls.

• Optimize compute resources and costs using cloud platforms like AWS, Azure, or GCP.

• Set up and manage experiment tracking and metadata management (e.g., MLflow, Weights & Biases).

• Collaborate cross-functionally with DevOps, Data Engineering, and Security teams to ensure robust ML operations.

Qualifications:

• Bachelor’s degree in Business Administration, Marketing, or a related field.

• 2+ years of experience in sales operations or analytics.

• Strong analytical skills and proficiency in data analysis tools.

• Familiarity with CRM systems (e.g., Salesforce) and reporting software.

• Excellent organizational and problem-solving skills.