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