3 SGS Jobs

Role Description

As an AI/ML Engineer at MySGS & Co., you will play a key role in designing, developing, and maintaining machine learning (ML) systems and AI-driven automation solutions. You will collaborate closely with product managers, data scientists, data engineers, architects, and other cross-functional teams to build scalable and production-ready ML models. Your role will focus on both ML development and MLOps, ensuring seamless deployment, monitoring, and automation of ML models in real-world applications.

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.

Skills and Qualifications

• 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