Teradata - MLOps Engineer - Data Modeling (4-8 yrs)

What Youll 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. Product Owners, Architects, Scrum

Who Youll Work With :

- Individual Contributor

- Works with project team members and architects and reports to Senior Engineering Manager

Required Skills :

- Bachelors 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 Youll 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