QE for AI Engineer

We are looking for someone who has validated AI systems in production - where outputs are non-deterministic failure modes are ambiguous and correctness is probabilistic.

You will be responsible for evaluating hardening and governing GenAI and agentic systems before and after they go live. This role sits at the intersection of engineering QA and AI safety ensuring that what we build works consistently reliably and safely - at scale.

You will partner closely with AI engineers architects and clients to define what correct means in an AI world and prove it.

AI Quality Engineering & Evaluation

• Define evaluation frameworks for LLMs RAG pipelines and multi-agent systems

• Design test strategies for non-deterministic systems (semantic correctness hallucination detection consistency checks)

• Build testing pipelines for LLM evaluation (offline online evals)

• Establish ground truth datasets benchmarks and scoring metrics (precision recall relevance factuality)

Agent & Workflow Validation

• Validate multi-agent orchestration flows.

• Test failure modes: hallucination incomplete reasoning.

• Simulate edge cases and adversarial inputs.

• Ensure robustness across multi-step workflows and chained reasoning tasks

RAG & Data Validation

• Validate end-to-end RAG pipelines:

  • Chunking quality

  • Embedding correctness

  • Retrieval relevance

  • Re-ranking effectiveness

• Detect and quantify RAG failure points (retrieval gaps stale data hallucinations)

• Ensure data lineage and traceability in AI responses

Guardrails Safety & Governance

• Ensure compliance with enterprise AI governance and auditability requirements

• Validate explainability and traceability of AI outputs

Automation & Tooling

• Automate prompt testing regression testing and response comparison
• Integrate AI validation into CI/CD pipelines

• Built or contributed to evaluation frameworks for LLM-based systems
• Tested RAG pipelines end-to-end and identified failure points
• Defined and executed non-deterministic test strategies
• Automated LLM evaluation or prompt regression pipelines
• Worked on agent-based or multi-step AI workflows
• Debugged incorrect or hallucinated model outputs using structured methods
• Established quality metrics where ground truth was unclear or evolving