Ignite the Future of Language with AI at Teradata!

What You'll Do: Shape the Way the World Understands Data

At Teradata, we're not just managing data; we're unleashing its full potential. Our ClearScape Analytics™ platform and pioneering Enterprise Vector Store are empowering the world's largest enterprises to derive unprecedented value from their most complex data. We're rapidly pushing the boundaries of what's possible with Artificial Intelligence, especially in the exciting realm of autonomous and agentic systems

We’re building intelligent systems that go far beyond automation — they observe, reason, adapt, and drive complex decision-making across large-scale enterprise environments. As a member of our AI engineering team, you’ll play a critical role in designing and deploying advanced AI agents that integrate deeply with business operations, turning data into insight, action, and measurable outcomes.

In this role, you’ll architect foundational components for production-grade AI systems — from agent frameworks and LLM pipelines to observability and evaluation layers that ensure reliability, accountability, and performance. You’ll be responsible not just for building models, but for making them measurable, debuggable, and trustworthy in real-world, high-stakes deployments.

You’ll work alongside a high-caliber team of AI researchers, engineers, and data scientists tackling some of the hardest problems in AI and enterprise software — from scalable multi-agent coordination and fine-tuned LLM applications, to real-time monitoring, drift detection, and closed-loop retraining systems.

If you're passionate about building intelligent systems that are not only powerful but observable, resilient, and production-ready, this role offers the opportunity to shape the future of enterprise AI from the ground up.

Who You'll Work With: Join Forces with the Best

Imagine collaborating daily with some of the brightest minds in the company – individuals who champion diversity, equity, and inclusion as fundamental to our success. You'll be part of a cohesive force, laser-focused on delivering high-quality, critical, and highly visible AI/ML functionality within the Teradata Vantage platform. Your insights will directly shape the future of our intelligent data solutions.

You'll report directly to the inspiring Sr. Manager, Software Engineering, who will champion your growth and empower your contributions.

Key Responsibilities:

• Design and deploy containerized applications using Docker and container orchestration platforms. Understand platform engineering.
• Implement and manage Kubernetes clusters for AI/ML workloads, ensuring high availability and scalability.
• Build and maintain CI/CD pipelines using Terraform for infrastructure as code and automated deployments.
• Collaborate on MLOps practices including model versioning, deployment, and monitoring.
• Manage and optimize LLM deployments, including inference endpoints and API gateways.
• Design infrastructure to support AI agent orchestration, prompt routing, and multi-agent collaboration.
• Implement Kubernetes-native AI microservices to support scalable inference and agent collaboration workloads.
• Spearhead the deployment, versioning, and monitoring of ML/LLM models using CI/CD pipelines and MLOps tools.
• Automate infrastructure provisioning and configuration using Terraform and Infrastructure as Code principles.
• Implement monitoring, logging, and observability solutions for containerized AI/ML applications.
• Work with LLM APIs and frameworks to support AI-driven features and integrations.
• Collaborate with data science and ML teams to operationalize models into production.
• Develop and maintain CI/CD workflows for continuous integration and deployment of ML models.
• Ensure system reliability, security, and performance at scale.
• Contribute to internal standards and best practices for cloud-native AI system development.
• Support containerized application deployments across development, staging, and production environments.

Required Qualifications:

• A Bachelor's or Master's degree in Computer Science, Engineering or a related field – your academic foundation is key.
• 2+ years of experience in cloud infrastructure, DevOps, or platform engineering.
• Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native architectures.
• **MUST HAVE**: Strong hands-on experience with containerization technologies (Docker, containerd).
• **BIG BONUS**: Experience with Kubernetes (K8s) cluster management and orchestration.
• Solid understanding of MLOps principles and practices for ML model lifecycle management.
• Knowledge of large language models (LLMs) and experience managing LLM deployments.
• Proven experience building and maintaining CI/CD pipelines using Terraform for infrastructure automation.
• Proficiency in scripting languages (Python, Bash, or similar) for automation.
• Understanding of networking, security, and infrastructure best practices.
• Familiarity with monitoring and observability tools (Prometheus, Grafana, ELK stack).
• Experience with version control systems (Git) and collaborative development workflows.

Preferred Qualifications:

• Experience with Kubernetes ecosystem tools (Helm, Operators, Service Mesh like Istio).
• Knowledge of AI/ML frameworks and model serving platforms (TensorFlow Serving, KServe, MLflow).
• Experience with LLM-specific tools and frameworks (LangChain, vector databases, prompt engineering).
• Familiarity with GitOps practices and tools (ArgoCD, Flux).
• Experience with infrastructure security and compliance standards.
• Contributions to open-source projects or technical communities.
• Strong problem-solving skills and ability to work in a collaborative team environment.
• Ownership mindset with a focus on reliability, scalability, and continuous improvement.

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