Site Reliability Engineer (GCP & Agentic AI)

UST · Artificial Intelligence · Information Technology · 2 days ago
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In office (Pune)

Confirmed on the employer's own hiring board on Oct 31, 2026. First seen by Alion on Oct 28, 2026. UST scores B on the Alion truth index.

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UST

UST is a leading global digital technology and transformation services company that provides innovative solutions to enterprise clients worldwide. Headquartered in Aliso Viejo, California, the firm specializes in digital consulting, cloud infrastructure, product engineering, and cybersecurity. Founded in 1998, it partners with major organizations across diverse industries to accelerate technological modernization and build agile, scalable business models.
ust.com · HQ: Aliso Viejo, United States · Information Technology · IT Outsourcing & Dedicated Teams · IT Consulting & Digital Transformation · 5000+ employees · Est. 1998
Tech stack of UST

We are looking for a hands-on Site Reliability Engineer responsible for ensuring production services are reliable, observable, and continuously improving. This role focuses on services including batch, streaming, microservices, AI/ML model serving, LLMOps pipelines, and agentic systems.

The ideal candidate brings Proficient-level expertise in GCP and agentic AI, using AI and agent-based capabilities responsibly to improve observability, incident investigation, operational workflows, and service reliability. The SRE remains accountable for validating AI-generated operational artifacts and production outcomes.

  • Own reliability initiatives end to end across multiple production applications on GCP, from issue detection and root-cause analysis through lasting remediation.
  • Apply agentic AI to observability design, operational workflows, and incident investigations, including orchestrating agents and sub-agents.
  • Use sound judgment when evaluating AI-generated runbooks, s, monitoring analysis, and operational recommendations.
  • Validate AI-generated artifacts before they are introduced into production and maintain clear accountability for operational outcomes.
  • Identify reliability gaps, cascading failure risks, SLO burn threats, and observability blind spots proactively.
  • Ensure AI/ML production systems have appropriate observability and human-in-the-loop escalation paths.
  • Drive continuous improvement of production reliability and operational practices.
  • Lead calmly and communicate clearly during incidents while collaborating effectively with Platform, ML, and Data teams.
  • Guide junior SREs in responsible operational practices and help maintain high reliability standards across the team.
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