Our Company

At Teradata, we believe that people thrive when empowered with better information. That’s why we built the most complete cloud analytics and data platform for AI. By delivering harmonized data, trusted AI, and faster innovation, we uplift and empower our customers—and our customers’ customers—to make better, more confident decisions. The world’s top companies across every major industry trust Teradata to improve business performance, enrich customer experiences, and fully integrate data across the enterprise.

What You’ll Do

• Accountable for the design, development and deployment of features, enhancements, bug-fix, applications and services for Terdata Modelops offering.
• Participate in team Scrum meetings, and interact with various stakeholders
• We are looking for an Engineer to drive the full lifecycle of machine learning models, from development to deployment and ongoing monitoring. This role requires strong experience in CI/CD workflows, model lifecycle management, and cloud-agnostic solutions. The ideal candidate will work on building and automating ML workflows, ensuring seamless integration and performance of models over time.
• Work with all development process contributors, e.g. Product Owners, Architects, Scrum

Who You’ll Work With

• Individual Contributor
• Works with project team members and architects and reports to Senior Engineering Manager

 Required Skills

• Bachelor’s or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Model Development and Deployment coordination with data scientists involves designing and implementing scalable, efficient, and reliable model training and evaluation processes.
• Optimize the performance and cost efficiency of machine learning workflows on AWS Sagemaker.
• Proficiency in programming languages such as Python, or Java.
• Experience with AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, Keras).
• Ability to work with both structured and unstructured data.
• Strong problem-solving skills and creativity.
• Excellent communication and teamwork skills.
• Familiarity with REST APIs and model serving architectures.
• Implement CI/CD pipelines for machine learning models using tools like AWS
• Code Pipeline /AWS Cloud formation / Terraform. Enabling seamless deployment and integration into production systems.
• Automate the build, testing, and deployment processes to ensure smooth and efficient delivery of updated models.
• Implement monitoring solutions to track the performance and behavior of deployed models in real-time.
• Set up alerts and notifications to proactively identify issues, such as model degradation or data drift, and take appropriate actions.
• Fine-tune the infrastructure settings, explore autoscaling capabilities, and utilize spot instances for cost-effective model training and inference.

What You’ll Bring

• Experience with cloud services (AWS, Google Cloud, Azure) related to AI and machine learning.
• Familiarity with machine learning algorithms and statistical modeling techniques.
• Contributions to open-source projects or active participation in the AI community

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