Agentic QA Engineer(1-4 yrs exp)
2 days ago
Position:
Agentic QA Engineer
Location:
Bangalore (Work from Office)
Timings:
Full-Time (As per company schedule)
Notice Period:
Immediate Joiner Preferred
Experience:
1–4 Years
About the Role
We are seeking a
skilled and detail-oriented Agentic QA Engineer
to test and validate
AI agents and LLM-based workflows
. The ideal candidate will bring a solid background in
software QA or AI testing
, with the ability to evaluate agentic behavior, ensure reliability, and maintain high system performance.
This role requires close collaboration with
developers, prompt engineers, and data scientists
to deliver robust, high-quality AI-driven systems that perform seamlessly in real-world scenarios.
Key Responsibilities
- Test and validate
AI agents
and
LLM workflows
for accuracy, correctness, and stability. - Design and execute
test cases
to assess agentic behavior, prompt precision, and outcome quality. - Utilize
DeepEval
or similar frameworks to build and automate
LLM evaluation pipelines
. - Identify, document, and track
bugs, performance issues, and logic inconsistencies
. - Collaborate with
cross-functional teams
to improve system quality and user experience. - Develop and maintain
QA documentation
including test plans, cases, and reports. - Ensure AI agents perform optimally across diverse contexts and workflows.
- Support the creation of
automated QA pipelines
for continuous validation and monitoring.
Skills & Qualifications
- 1–4 years of experience in
Quality Assurance
,
Software Testing
, or
AI Testing
. - Strong understanding of
Generative AI
,
LLM agents
, and
autonomous workflows
. - Experience in
manual testing
with familiarity in
automation frameworks
. - Hands-on experience or working knowledge of
DeepEval
for LLM evaluation. - Proficiency in
Java
or
Python
for test automation and scripting. - Excellent
analytical
,
debugging
,
documentation
, and
communication
skills.
Good to Have
- Experience with
LangChain
,
CrewAI
,
AutoGen
, or similar
agentic frameworks
. - Understanding of
AI evaluation metrics
(accuracy, coherence, relevance, hallucination detection). - Exposure to
CI/CD pipelines
,
test automation
, or
model monitoring tools
.
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