Senior AI Evals Engineer

The Senior AI Evals Test Engineer is a hands-on technical role responsible for defining developing and executing robust evaluation frameworks and test strategies for AI agents built using the Google Agent Development Kit (ADK) or similar sort of agentic frameworks. This role bridges AI development and reliable deployment ensuring our agents are safe ethical effective and meet high-quality performance standards. A significant focus of the role is on Responsible AI and Safety Evals including adversarial testing red-teaming bias detection and toxicity measurement. The engineer will design scalable evaluation pipelines build synthetic test environments and work closely with AI/ML Engineers Researchers and policy teams to ensure agents are production-ready and compliant with regulatory and ethical standards. The role is 70% Automation and 30% Manual Testing and sits within the Applied AI: Test Engineering team as a key technical authority.

• Design and build agent evaluation frameworks create end-to-end infrastructure for evaluating LLM-based agents across correctness latency cost safety and task completion rate
• Build synthetic testing environments and simulation strategies to stress-test AI agents under diverse real-world conditions with specific focus on agents built on Google ADK and related Google AI/ML services (e.g. Vertex AI Gemini models)
• Build golden datasets and test harnesses curate domain-specific evaluation sets for Rubriks product verticals (data security SaaS protection cyber recovery compliance)
• Establish eval-as-a-service build and enhance centralized evaluation platform that any IT team members can self-serve to test their agents against standardized and custom benchmarks
• Automate regression and drift detection implement CI/CD-integrated eval pipelines that catch regressions on every model or prompt change before production deployment
• Define eval taxonomies and rubrics establish structured scoring criteria for agentic workflows (tool use accuracy multi-step reasoning hallucination rate policy adherence error recovery)
• Instrument production observability build real-time monitoring for agent quality in production: success rates user corrections fallback triggers escalation patterns
• Benchmark and select models run structured model comparisons (cost quality latency trade-offs) to inform model selection decisions across agent use cases
• Analyze and interpret evaluation results providing clear actionable quality and safety reports to stakeholders and development teams with specific focus on risk metrics trend analysis and mitigation recommendations
• Define comprehensive end-to-end QA strategies functional integration regression and user acceptance testing (UAT) tailored for conversational and goal-oriented AI agents with tool-use memory and multi-turn reasoning capabilities
• Develop and maintain detailed test artifacts (test plans test cases test scenarios automated test suites) covering agent features tool invocation memory persistence and reasoning accuracy across evolving model versions
• Identify document prioritize and track defects performance regressions and alignment failures using Jira collaborating closely with Applied AI Engineers to analyze root causes and validate fixes
• Evaluate new technologies and patterns (e.g. new tools for Agent Evals) run proofs-of-concept and influence the platform roadmap
• Partner with policy and legal teams to ensure agent evaluations systematically cover regulatory compliance fairness objectives and emerging AI governance requirements
• Provide technical leadership to engineers including solutioning design reviews code reviews pair programming and mentoring of junior and contract engineers
• Collaborate cross-functionally work closely with ML engineers product teams platform engineers and security to embed eval-driven quality gates into the agent development lifecycle

• Bachelors or Masters 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 build evaluation systems across multiple AI/ML domains conversational agents or Large Language Models (LLMs)
• 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 or agentic workflows
• 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 ADLC model

• Deep hands-on experience with LLM evaluation familiarity with frameworks and libraries such as LangSmith DeepEvalLLM as a Judge Promptfoo RAGAS Hugging Face evaluation tooling custom eval harnesses
• Strong understanding of agentic AI patterns tool use function calling multi-step planning ReAct/CoT reasoning chains retrieval-augmented generation (RAG)
• Production experience with eval pipelines building automated CI-integrated evaluation infrastructure (not just notebooks)
• Proficiency in Python strong scripting and tooling skills; experience with pytest async patterns and data processing at scale
• Familiarity with Google Cloud Platform (GCP) or any Cloud Service relevant to AI/ML (e.g. Vertex AI) and experience integrating testing into MLOps workflows
• Statistical literacy ability to design experiments interpret eval metrics with appropriate confidence and avoid common pitfalls (overfitting to benchmarks metric gaming)
• Enterprise product sensibility understanding of how AI quality translates to customer trust in a B2B/enterprise context (security compliance reliability SLAs)
• Excellent communication ability to present eval results and quality trade-offs to engineering leadership articulate risks and safety findings to non-technical audiences and drive alignment on ship/no-ship decisions
• Hands-on experience with CI/CD pipelines and Git-based workflows including automated testing static analysis deployment automation and environment configuration for our DevTestOps

• 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)

• Deep Learning/NLP Knowledge: Fundamental understanding of how LLMs work including concepts like RAG prompting and the technical challenges related to alignment
• Data Analysis: Experience with data analysis and visualization tools (e.g. Pandas SQL) for analyzing evaluation and safety results
• Policy Background: Familiarity with AI ethics principles and industry standards for responsible AI development
• Garbage-in/Garbage-out (GIGO) Tools: Any tool or framework that specializes in testing the models response to highly ambiguous misleading or malformed inputs to test for robustness and safety failures
• Experience testing complex APIs especially for tool-integration and function-calling scenarios in agentic workflows
• Experience with prompt injection (Must) and data poisoning skills is a plus
• Certifications in AI/ML cloud platforms (GCP AWS) AWS Machine Learning Specialty Google Professional ML Engineer specializations and software testing methodologies

• Proven ability to lead without direct authority influencing cross-functional teams and driving technical decisions through clarity and data rather than hierarchy.
• Experience working in agile environments collaborating closely with product owners scrum teams and distributed stakeholders across time zones.
• Excellent communication and documentation skills comfortable presenting solutions and trade-offs to engineers managers and senior business stakeholders.
• Strong architectural thinking and problem-solving skills including the ability to decompose complex business processes into simple solutions

• Experience evaluating agents in cybersecurity data protection or infrastructure domains
• Familiarity with Rubriks product portfolio or similar enterprise data management platforms
• Hands-on with human-in-the-loop eval workflows (annotation platforms inter-rater reliability)
• Experience with model fine-tuning evaluation (pre/post quality comparison)
• Background in building internal developer platforms or tooling teams
• Contributions to open-source eval frameworks or published work on AI evaluation methodology
• Drive red-teaming and adversarial testing design adversarial evaluation suites for jailbreak resistance data leakage prevention and boundary enforcement in enterprise-grade agents

Rubrik (RBRK) the Security and AI Operations Company leads at the intersection of data protection cyber resilience and enterprise AI acceleration. Rubrik Security Cloud delivers complete cyber resilience by securing monitoring and recovering data identities and workloads across clouds. Rubrik Agent Cloud accelerates trusted AI agent deployments at scale by monitoring and auditing agentic actions enforcing real-time guardrails fine-tuning for accuracy and undoing agentic mistakes.

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