Principal AI Agentic Engineer for Observability & Autonomy

Teradata; San Jose (CA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Teradata in San Jose, CA is seeking a Senior AI Platform Engineer to lead design and development of AI-native agentic workflows that boost engineering productivity, observability intelligence, and end-to-end operational excellence across Teradata’s AI Platform.

You will architect and deploy AI agents for observability, automated root-cause analysis, anomaly detection, and productivity optimization, applying LLMs and reasoning frameworks to automate complex debugging and diagnostics.

Qualifications

• 8+ years of experience in software engineering with deep exposure to AI/ML systems in production environments.
• Proven experience building AI agents, LLM-driven systems, or autonomous workflows—not just consuming AI APIs.
• Strong programming expertise in Python (required) and at least one of Java or Go.

Responsibilities

• Lead design and development of AI-native agentic workflows that enhance engineering productivity, observability intelligence, and end-to-end operational excellence across Teradata’s AI Platform.
• Architect and build AI agents for observability, automated root cause analysis, anomaly detection, and engineering productivity optimization.
• Design agentic workflows that integrate across telemetry pipelines, application stacks, and cloud infrastructure.
• Develop AI-driven tooling to accelerate development velocity, reduce operational toil, and improve MTTR.
• Partner across engineering teams to embed intelligent automation into CI/CD, DevOps, and support workflows.

Skills

Python

Java or Go

AI/ML systems

LLM-driven systems

Tools

OpenTelemetry

Prometheus

Job description

Teradata in San Jose, CA is seeking a Senior AI Platform Engineer to lead design and development of AI-native agentic workflows that boost engineering productivity, observability intelligence, and end-to-end operational excellence across Teradata’s AI Platform.

You will architect and deploy AI agents for observability, automated root-cause analysis, anomaly detection, and productivity optimization, applying LLMs and reasoning frameworks to automate complex debugging and diagnostics.