Overview:
ML Engineers are the engineer AI/ML systems. They leverage their expertise in software engineering, machine learning, data engineering to design, develop, and deploy AI/ML models into real-world applications.
Responsibilities:
- Engineer and deploy machine learning models from development to production environments, ensuring real-world impact and reliability.
- Build and maintain scalable data pipelines to support continuous model training, inference, and integration with manufacturing systems.
- Monitor model performance using comprehensive logging, diagnostics, and automated retraining to maintain optimal accuracy and efficiency.
- Optimize model execution for faster inference and resource utilization in high-volume manufacturing environments.
- Collaborate closely with data scientists, data engineers, and product managers to understand requirements, address technical challenges, and facilitate smooth model integration.
- Clearly communicate technical trade-offs, limitations, and solutions with cross-functional teams, influencing technology adoption and project outcomes.
- Document engineering processes, model architectures, and deployment strategies to support maintainability and future enhancements.
- Diagnose bottlenecks in system performance and model accuracy, applying critical thinking and innovative problem-solving to optimize solutions.
- Contribute to continuous improvement initiatives, including automation, deployment pipeline enhancements, and advanced model lifecycle management.
Skill Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
- 4+ years of proven experience designing, deploying, and maintaining machine learning models in production, preferably within a manufacturing context.
- Strong proficiency in Python and relevant ML frameworks (TensorFlow, PyTorch, scikit-learn).
- Expertise in building and managing data pipelines using tools such as Apache Spark, Airflow, or similar.
- Hands-on experience with cloud platforms (AWS, Azure, GCP) and CI/CD pipelines for model deployment and automation.
- Solid understanding of software engineering principles, including version control (Git), code repositories, and system optimization.
- Excellent written and verbal communication skills, with the ability to document technical processes and collaborate across teams.
Other Requirements (Optional)
- Certifications in cloud computing, data engineering, or machine learning (e.g., AWS Certified Machine Learning, Google Professional ML Engineer).
- Experience with manufacturing systems, IoT data integration, or industrial automation.
- Familiarity with monitoring tools (Prometheus, Grafana) and logging frameworks.
- Prior exposure to Agile methodologies and cross-functional project delivery.
