Information Technology
Full-Time
SGS & Co
Overview
About The Role
You will be responsible for optimizing end-to-end ML pipelines, automating workflows, managing model lifecycle operations (MLOps), and ensuring AI systems are scalable and cost-efficient. You will embrace a build-measure-learn approach, continuously iterating and improving models for performance and reliability.
Responsibilities
You will be responsible for optimizing end-to-end ML pipelines, automating workflows, managing model lifecycle operations (MLOps), and ensuring AI systems are scalable and cost-efficient. You will embrace a build-measure-learn approach, continuously iterating and improving models for performance and reliability.
Responsibilities
- Design, develop, and maintain ML models to solve business challenges and drive automation.
- Implement and optimize ML algorithms for efficiency, scalability, and AI-powered insights.
- Conduct experiments, A/B testing, and model evaluations to improve performance.
- Develop, containerize, and deploy AI/ML systems in production environments using best practices.
- Automate and streamline ML pipelines, ensuring smooth transitions from development to production.
- Monitor and troubleshoot the performance, accuracy, and drift of ML models in production.
- Execute and automate model validation tests, ensuring robustness and reliability.
- Optimize training and inference workflows, enhancing model efficiency and speed.
- Manage model versioning, deployment strategies, and rollback mechanisms.
- Implement and maintain CI/CD pipelines for ML models, ensuring smooth integration with engineering workflows.
- Review code changes, pull requests, and pipeline configurations to uphold quality standards.
- Stay updated with emerging AI/ML technologies, MLOps best practices, and cloud-based ML platforms.
- Strong programming skills in Python or R, with experience in ML frameworks (TensorFlow, PyTorch, Scikit-learn).
- Experience deploying and maintaining ML models using Docker, Kubernetes, and cloud-based AI services (AWS Sagemaker, GCP Vertex AI, Azure ML).
- Solid understanding of MLOps principles, including CI/CD for ML models, model monitoring, and automated retraining.
- Knowledge of data engineering principles, data preprocessing, and feature engineering for ML pipelines.
- Familiarity with workflow orchestration tools.
- Experience with real-time model serving and API deployment.
- Strong analytical and problem-solving skills with a keen attention to detail.
- Ability to collaborate cross-functionally and work in a fast-paced AI-driven environment.
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