Senior AI evals engineer at Rubrik Bengaluru, building the evaluation frameworks and test strategies that make AI agents safe to deploy.

This full-time Bengaluru role was posted on 15 October 2026. The listing describes a hands-on technical role defining, developing and executing evaluation frameworks and test strategies for AI agents built using the Google Agent Development Kit (ADK) or similar agentic frameworks, bridging AI development and reliable deployment so agents are safe, ethical, effective and meet performance standards. Responsibilities include designing and building agent evaluation frameworks - end-to-end infrastructure for evaluating LLM-based agents. Requirements include a bachelor's or master's degree in a relevant field or equivalent practical experience.

About Rubrik

Rubrik builds data security software: backup and recovery, ransomware protection and data posture management for enterprise data across cloud and on-premise systems. Its current listings describe a push into AI, including internal enterprise-AI platforms and agentic systems.

Bengaluru is one of Rubrik's main engineering centres, hosting teams across core engineering, IT, security and AI work, per its listings. Learn more on Rubrik's website.

The role in plain terms

Evals are the discipline that separates demo agents from deployed ones, and this listing treats it as a first-class engineering function: building the infrastructure to measure whether agents work, not just running test cases. The mention of responsible AI as a significant focus, alongside safety and effectiveness, tells you the bar includes more than accuracy. Experience with agentic frameworks like Google ADK is named directly, so hands-on agent work matters more than general QA background.

The team and the work

Per the listing, the role sits between AI development and deployment - you define what 'good' means for agents and build the machinery that enforces it. This is a relatively new role type industry-wide, so expect to help define the practice, not inherit it.

What the employer asks for

These requirements are quoted from the employer's listing, kept in their exact words so eligibility details are not distorted:

Education & Experience
Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or equivalent practical experience
8+ years of overall software Test engineering experience , with 5+ years focused on test engineering, QA automation, or developer tooling, and demonstrated ability to...
2+ years working with ML/AI systems, building , testing, or evaluating models in production environments, including hands-on experience with LLM-based applications, prompt engineering, RAG pipelines,...
2+ years as a senior/lead, owning evaluation strategy, quality frameworks, and technical decisions for complex AI agent systems in an enterprise product environment
1+ years in designing or operating evaluation frameworks specifically for generative AI / LLM agents, including benchmarking, red-teaming, and production quality monitoring in the AI...
Deep hands-on experience with LLM evaluation — familiarity with frameworks and libraries such as LangSmith, DeepEval,LLM as a Judge, Promptfoo, RAGAS, Hugging Face evaluation tooling,...
Strong understanding of agentic AI patterns — tool use, function calling, multi-step planning, ReAct/CoT reasoning chains, retrieval-augmented generation (RAG)

Tools and technologies named in the listing

Languages: Python(Must), TypeScript, Java (Optional)
Test Automation:Selenium, Playwright
LLM Providers: Anthropic Claude, OpenAI, Azure OpenAI, open-source models
Eval Tooling: DeepEvals(Must), LLM as a Judge (Must), Promptfoo/RAGAS/LangSmith/ Custom harnesses
Agent Evals Frameworks/libraries: LangGraph, Claude Agent SDK, custom orchestrators
Infrastructure: DevOps (Basic), Kubernetes, CI/CD (Jenkins/GitHub Actions), observability stacks (Datadog/Grafana)

Who this role suits

Engineers with test automation or ML evaluation backgrounds who have worked hands-on with LLM agents or agentic frameworks. Not for manual QA professionals or ML engineers without evaluation and testing depth.

Practical notes

For AI evals roles, expect questions on how you would measure an agent's correctness, safety and regression risk, plus practical discussion of eval infrastructure. Familiarity with ADK or comparable frameworks is a stated expectation - get hands-on before the loop.

Working at Rubrik

Engineering at Rubrik covers distributed systems for data protection at enterprise scale, plus newer AI platform teams. Listings emphasize production ownership and hands-on depth in systems languages and Kubernetes.

How to apply

Apply directly on the employer's site: Senior AI Evals Engineer at Rubrik. We found this listing on the Greenhouse public job board (rubrik.com), where it was posted on 15 Oct 2026 and was still open when we checked on 30 Oct 2026. Before applying, re-read the employer's own description in full, since eligibility details, deadlines and process steps can change at any time and the employer's page is always the final word. Tailor your resume to the specific requirements above rather than sending a generic one: mirror the skills and tools the listing names where you genuinely have them. A short, specific note on why this team and this problem interest you goes further than a long generic cover letter. Never pay a fee to apply or interview; genuine employers, including this one, do not charge candidates.

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