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Test Automation Architect

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Onsite

Test Automation Architect

Centific

Test Automation Architect

Skills

Google Cloud PlatformProduct StrategyPlaywrightCypressAIAWSAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Hyderabad, Telangana, India

Work mode:

Onsite

Published on:

11 August 2026

What they need


Architecture & Strategy

  • Design and own the enterprise-wide Test Architecture for AI products including LLM-based applications, RAG pipelines, and autonomous agentic systems.
  • Define quality gates, evaluation metrics, and acceptance criteria aligned with AI model behaviour, safety, and regulatory requirements.
  • Architect scalable, cloud-native test infrastructure on Azure / AWS / GCP supporting continuous evaluation of non-deterministic AI outputs.
  • Lead the selection and integration of AI testing toolchains into CI/CD pipelines (e.g. Playwright, Cypress, LangSmith, Evals frameworks).

Agentic AI & LLM Testing

  • Build evaluation harnesses for multi-agent systems — covering agent reasoning chains, tool-use accuracy, memory recall, and goal completion rates.
  • Design test strategies for agentic workflows built on frameworks such as AutoGen, LangGraph, CrewAI, or custom orchestration layers.
  • Develop adversarial testing suites: prompt injection attacks, jailbreak probes, hallucination detection, and bias evaluation.
  • Establish ground-truth benchmarking datasets and model regression pipelines to track LLM quality across fine-tuning cycles.

Product & Platform Engineering

  • Drive design and delivery of internal testing products: AI Eval Platforms, automated quality dashboards, and AI observability tooling.
  • Contribute to the product roadmap for quality tooling — writing PRDs, shaping backlogs, and aligning engineering with business outcomes.
  • Integrate performance testing strategies (K6, JMeter) for AI API endpoints, latency SLAs, and throughput benchmarks.

Leadership & Enablement

  • Lead, mentor, and grow a team of 8–15 QE engineers across automation, AI eval, and performance disciplines.
  • Establish CoE standards: coding conventions, test design patterns (POM, BDD), and documentation norms across QE squads.
  • Partner with AI Product Managers, Data Scientists, and MLOps engineers to co-own quality across the full model lifecycle.
  • Present architecture decisions and quality health metrics to senior leadership and external stakeholders.

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