Our Company
At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.
About Teradata's AI Strategy
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.
This role directly supports that strategy by ensuring the reliability, accessibility, and optimal performance of the AI tool stack that powers Teradata's internal AI capabilities.
What We Do
The AI Tech Tool Stack Support Analyst / Specialist is responsible for the day-to-day operational support, configuration, monitoring, and troubleshooting of Teradata's AI stack. This includes Teradata-native tools such as MCP Server, Teradata AI assistant and AI Microservices, as well as integrated third-party components like N8N, JupyterHub, Airflow, JAMS and LLM provider APIs (Anthropic Claude, Azure OpenAI, Amazon Bedrock, Google Gemini).
The analyst serves as a front-line technical resource for data scientists, Data engineers, and business analysts, ensuring they can effectively leverage the AI tool stack to move workloads from experimentation to production. This role operates within the framework of Teradata's governance, security, and compliance standards, contributing to the organization's ability to deliver trusted AI at scale.
What You’ll Do
- Enterprise AgentStack and Agentic AI Support
Provide technical support for Teradata's AI tools MCP Server. This involves assisting teams in building, deploying, and ensuring agents have proper access to Teradata tools, prompts, and data resources.
- MCP Server: Maintain and support the Model Context Protocol Server that provides agents with semantic access to enterprise data, curated prompts, and Teradata platform capabilities including SQL tools, DBA tools, Enterprise Feature Store, Quality Tools, vector store, RAG, and Teradata AI Studio tools.
- Pre-built Agents: Support deployment and configuration of Teradata developed agents using workflow tools like n8n.
- Third-Party AI Tool Integration and LLM Provider Management
Support for integration between Teradata's AI platform and external AI service providers and open-source tools that form part of the broader AI tool stack.
- LLM API Integrations: Configure and maintain connections to Azure OpenAI, Amazon Bedrock, and Google Gemini endpoints. Monitor API health, manage authentication credentials and secrets, and troubleshoot connectivity or compatibility issues. Track usage analytics and spend per project or model.
- Open-Source Ecosystem: Support the integration and maintenance of open-source tools within the Teradata AI Studio ecosystem, including Airflow (workflow orchestration), Devpi (Python package management).
- User Support and Enablement
Serve as the primary point of contact for internal users and customer-facing teams who rely on the AI tool stack. Provide responsive, knowledgeable support that minimizes friction and accelerates AI adoption.
- Ticket Resolution: Resolve support tickets related to environment setup and configuration, library and dependency conflicts, notebook failures, , data access and permissions, and agent workflow failures.
- Documentation: Create and maintain runbooks, FAQs, troubleshooting guides, and knowledge base articles specific to Teradata's AI tool stack. Document known issues, workarounds, and best practices.
- Onboarding and Training: Conduct onboarding sessions for new users of MCP Server or TD AI assistant and N8N tools. Develop self-service guides and workshops that drive adoption and reduce dependency on support.
- Stakeholder Communication: Communicate proactively about planned maintenance, known issues, platform updates, and new feature releases to user communities.
- Monitoring, Performance, and Incident Management
Implement and manage monitoring and alerting AI platform components to ensure uptime, performance, and rapid incident response.
- Monitoring: Configure and maintain monitoring dashboards for AI workload health, compute utilization, model serving latency, and pipeline execution status. Leverage Teradata's Monitoring Agent and standard observability tools.
- Incident Response: Participate in incident response for AI platform outages, degraded performance, or pipeline failures. Perform initial triage, escalate as needed, and document root cause and remediation steps.
- Performance Optimization: Identify bottlenecks in compute, storage, or networking that impact model training or inference. Recommend instance right-sizing, autoscaling adjustments, and caching improvements.
- Automation and Operational Efficiency
Develop automation scripts and processes to improve operational efficiency, reduce manual toil, and ensure consistency across the AI tool stack.
- Scripting: Write automation scripts (Python, Bash, SQL) for routine tasks such as environment provisioning, user account management, log aggregation, backup verification, and cost reporting.
- Infrastructure as Code: Contribute to Infrastructure-as-Code templates for reproducible AI environment deployments, particularly for Teradata Factory on-premises configurations.
- Process Improvement: Identify recurring support issues and develop preventive solutions, self-service tools, or automated remediation workflows to reduce ticket volume.
Key Tools and Technologies
ModelOps, Teradata AI studio, Enterprise Vector Store, AI Microservices
MCP Server, and workflow tool like N8N
Azure OpenAI, Amazon Bedrock, Google Gemini, Claude
JupyterHub, Airflow, Devpi, Python, SQL
platform observability tools Prometheus, grafana
Python, Bash, SQL, Infrastructure-as-Code frameworks
What Makes You a Qualified Candidate
- Bachelor's degree in Computer Science, Information Technology, Data Science, or a related technical field, or equivalent practical experience.
- 2-4 years of experience in technical support, platform operations, or IT infrastructure roles, preferably in environments involving AI or advanced analytics platforms.
- Working knowledge of SQL and relational database concepts, particularly within Teradata Cloud or equivalent enterprise data platforms.
- Experience with Python scripting for automation and troubleshooting purposes.
- Familiarity with cloud platforms (AWS, Azure, or GCP) and basic cloud infrastructure concepts.
- Understanding of AI/ML concepts including model training, deployment, inference, and lifecycle management.
- Strong troubleshooting and analytical skills with the ability to diagnose complex technical issues across multiple integrated systems.
- Excellent written and verbal communication skills, with the ability to create clear documentation and interact effectively with both technical and non-technical stakeholders.
Preferred Qualifications
- Experience supporting or administering JupyterHub, Apache Airflow, or similar data science workflow tools.
- Familiarity with LLM APIs (OpenAI, Anthropic, Google Gemini) and embedding models
- Knowledge of containerization (Docker, Kubernetes) and Infrastructure-as-Code (Terraform, CloudFormation) Linux/Windows administration.
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