Position: GenAI Test Engineer
Location: Halifax – Hybrid
Skills:
- GenAI, LLM, RAG and agentic AI testing
- Python-based API and test automation, including PyTest or Robot Framework
- LLM evaluation, prompt-injection and hallucination testing, CI/CD and observability
Key Responsibilities
- Develop and execute comprehensive test strategies for GenAI applications, including LLM-powered solutions, RAG pipelines, chatbots, copilots and agentic AI workflows.
- Validate functional and non-functional requirements across prompts, model responses, APIs, user interfaces, integrations and end-to-end business workflows.
- Create reusable evaluation datasets, test cases and scoring rubrics for relevance, groundedness, factual accuracy, completeness, coherence, instruction adherence and response consistency.
- Test RAG solutions by evaluating retrieval quality, context precision and recall, source attribution, answer faithfulness and resistance to unsupported or hallucinated responses.
- Validate agentic AI behaviour, including tool selection, function calling, reasoning flows, multi-step task completion, memory handling, fallback paths and failure recovery.
- Perform adversarial, security and safety testing for prompt injection, jailbreaks, sensitive-data exposure, toxic or prohibited content, authorization bypass and insecure tool use.
- Conduct performance, scalability and reliability testing for response latency, throughput, token usage, cost, concurrency, rate limits and degradation under load.
- Build automated regression suites in Python and integrate GenAI evaluations, API tests and quality gates into CI/CD pipelines.
- Monitor production quality through traces, evaluation metrics, user feedback and drift indicators; identify defects, analyse root causes and support remediation.
- Collaborate with product, development, data science, security and business teams to define acceptance criteria, test coverage and release-readiness standards.
Required Qualifications and Skills
- Strong experience in software quality engineering, test strategy, test design, defect management and automation for enterprise applications.
- Hands-on knowledge of GenAI concepts, LLM behaviour, prompt engineering, embeddings, vector databases, RAG architectures and agentic AI systems.
- Proficiency in Python and experience with automation frameworks such as PyTest or Robot Framework, together with API testing tools and libraries.
- Experience evaluating LLM outputs using deterministic checks, semantic similarity, model-based evaluation and human-review workflows.
- Practical experience testing hallucinations, bias, robustness, non-deterministic behaviour, prompt injection, guardrails and content-safety controls.
- Working knowledge of GenAI evaluation and observability tools such as Ragas, DeepEval, Promptfoo, LangSmith, Arize Phoenix or equivalent platforms.
- Strong SQL skills and experience validating source data, document ingestion, chunking, metadata, embeddings and retrieval results.
- Experience with GitHub, GitHub Actions or comparable CI/CD tools; familiarity with Docker, Kubernetes, AWS or Azure is desirable.
- Ability to define measurable acceptance thresholds, analyse evaluation results and communicate quality risks clearly to technical and business stakeholders.
- Strong analytical, investigative and problem-solving skills, with attention to responsible AI, privacy, security and regulatory expectations.
Regards
Patrick Fernandez
Talent Acquisition Group - Strategic Recruitment Manager
