Company Overview :

We are a leading SAP consulting firm, specializing in providing comprehensive SAP solutions to businesses across various industries. Our expertise spans implementation, optimization, and support services, enabling our clients to leverage the full potential of their SAP investments. We operate globally, serving a diverse clientele ranging from mid-sized enterprises to large multinational corporations.

Role Overview :

As an ML Ops Engineer, you will be instrumental in building, deploying, and maintaining machine learning models within the SAP ecosystem. You will collaborate closely with data scientists, SAP consultants, and infrastructure teams to ensure the seamless integration of ML solutions into business processes. Your work will directly impact the efficiency and effectiveness of our clients' operations by automating tasks, improving decision-making, and driving innovation through data-driven insights.

Key Responsibilities :

- Design and implement robust, scalable, and automated ML pipelines for training, validation, and deployment of machine learning models.

- Collaborate with data scientists to package and deploy ML models into production environments, ensuring high availability and performance.

- Monitor model performance in production, identify and troubleshoot issues, and implement retraining strategies to maintain accuracy.

- Develop and maintain infrastructure as code (IaC) using tools like Terraform or CloudFormation to automate the provisioning and management of ML infrastructure.

- Implement CI/CD pipelines for ML model deployment, ensuring version control, testing, and rollback capabilities.

- Work with SAP consultants to integrate ML models into SAP systems, leveraging APIs and other integration technologies.

- Ensure compliance with data privacy and security regulations throughout the ML lifecycle.

- Document ML Ops processes and best practices to facilitate knowledge sharing and collaboration within the team.

Required Skillset :

- Demonstrated ability to build and deploy machine learning models into production environments using tools like Docker, Kubernetes, and cloud platforms (AWS, Azure, GCP).

- Proven experience in implementing CI/CD pipelines for ML model deployment using tools like Jenkins, GitLab CI, or Azure DevOps.

- Strong understanding of data engineering principles and experience with data processing frameworks like Spark or Hadoop.

- Proficiency in programming languages such as Python and experience with ML libraries like TensorFlow, PyTorch, or scikit-learn.

- Excellent communication and collaboration skills, with the ability to effectively communicate complex technical concepts to both technical and non-technical audiences.

- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.