MLOps Engineer - Data Modeling (4-7 yrs)

Job Description :

Company Description :

Our client provides a comprehensive suite of financial solutions across Loans, Investments, Insurance, and Payments to serve the diverse needs of customers across their lifecycles.

Role Description :

As an ML Ops Engineer, you will be responsible for deploying, managing, and scaling machine learning models in production environments.

You will work closely with data scientists, developers, and other engineering teams to streamline the ML lifecycle, ensuring smooth operations, and maintaining system reliability.

Key Responsibilities :

- Design, deploy, and manage ML models and pipelines in production environments.

- Collaborate with data scientists and software engineers to integrate machine learning models into real-time applications.

- Develop and automate CI/CD pipelines for ML workflows.

- Monitor the performance and reliability of deployed models, identifying issues and optimizing them for production.

- Implement and manage data versioning, model versioning, and rollback strategies.

- Leverage cloud platforms (AWS, Azure, GCP) for scalable ML infrastructure.

- Collaborate with cross-functional teams to ensure best practices in ML system design, monitoring, and performance tuning.

Requirements :

- 4 - 7 years of experience in ML Ops or related fields, with a strong background in software engineering.

- Hands-on experience with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.

- Proficiency in cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).

- Strong knowledge of CI/CD pipelines and version control systems (Git, Jenkins, etc.

- Experience with data pipeline orchestration tools (Airflow, Kubeflow, etc.

- Familiarity with infrastructure automation tools (Terraform, Ansible).