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AI Quality Engineer (Contract)

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AI Quality Engineer (Contract)

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AI Quality Engineer (Contract)

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Google Cloud PlatformMachine LearningAIAWSAzure

2 months ago

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Important information


Contract type:

Freelance / Contract

Location:

Hyderabad, Telangana, India

Work mode:

Onsite

Published on:

28 August 2026

What they need


AI Quality EngineerLocation:- Hyderabad
Work Mode:- WFO (5 days a week)
Role Type:- Contractual (3 months and extension would depend on project requirement and performance)

Key Responsibilities

  • Test and validate AI-generated insights, recommendations, and decision-making workflows.
  • Evaluate LLM and RAG systems for accuracy, relevance, consistency, factuality, and hallucinations.
  • Validate retrieval quality, context relevance, grounding, and response quality in RAG systems.
  • Test AI agents and autonomous workflows across functional, negative, and edge-case scenarios.
  • Define AI evaluation criteria, test datasets, quality metrics, and validation processes.
  • Perform regression testing for models, prompts, RAG configurations, and AI workflows.
  • Collaborate with AI/ML engineers to identify issues and improve AI system quality.

Requirements

Required Skills

  • Strong understanding of AI/ML and Generative AI testing.
  • Hands-on experience testing LLM and RAG-based applications.
  • Knowledge of LLM evaluation, hallucination detection, relevance, and response quality.
  • Understanding of AI agents and recommendation systems.
  • Strong analytical and problem-solving skills.

Good to Have

  • Experience with PyTest and automated testing frameworks.
  • Experience building automated AI evaluation and regression frameworks.
  • Familiarity with tools such as RAGAS, DeepEval, LangSmith, or equivalent.
  • Experience with CI/CD-based test automation, performance testing, or AI guardrails.
  • Familiarity with cloud platforms (AWS/Azure/GCP) and observability tools.

Success Metrics

  • High accuracy, relevance, and reliability of AI outputs.
  • Strong evaluation coverage across critical AI workflows.
  • Early detection and reduction of hallucinations and AI regressions.
  • Reduced production AI quality issues.
  • Increased confidence and trust in AI-generated insights and recommendations.

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