Validates data movement, mappings, transformations, APIs, migrations, AI-agent workflows, business rules, guardrails, and user-interface outputs. Develops integration, regression, edge-case, and AI-output tests; verifies data integrity and schema conformance; evaluates agent decisions, tool usage, fallbacks, hallucinations, and human-review handoffs. Investigates defect sources across systems and communicates quality risks and findings to product, engineering, data, and business stakeholders.
Role– AI System Quality Assurance Engineer –
Data & Agentic AI
Experience: 1–3 years
Positions: 2
Role Summary - This role will focus on
validating data movement, mapping, transformation, and integrity across
migration testing and the integration between our agentic AI quoting platform
and SubmissionLink. AI agents consume Small Business Owner information
provided through SubmissionLink and backed by structured data models
to create insurance applications.
The role will verify what data is being used, how it moves
through the system, and whether it is correctly validated at each
stage, from source payloads and migration outputs through APIs, AI agents,
business rules, guardrails, and the user interface. It requires strong API,
integration, and data-validation skills, along with practical exposure to
agentic testing and validation of AI-generated outputs.
The candidate must have strong communication skills
and be capable of working directly with US-based Product, Data Intelligence,
Engineering, and business stakeholders.
Key Responsibilities -
- Understand
what data is being used and map how it moves through each stage of the
system pipeline
- Perform
data validation during migration testing, including source-to-target
comparison, completeness checks, accuracy checks, and transformation
validation
- Validate SubmissionLink data
against expected Small Business Owner information and downstream
insurance-application outputs
- Verify
that AI agents correctly consume, interpret, and
apply SubmissionLink data when creating insurance
applications
- Validate
data mapping and transformation across source systems, APIs, AI agents,
business rules, guardrails, and the UI
- Build
or execute validation scripts to check data integrity, completeness,
schema conformance, and transformation accuracy across the pipeline
- Test
agentic workflows and validate AI-agent decisions, tool usage,
fallback behavior, exception handling, and human-review
handoffs
- Define
and execute validations for AI-generated outputs, including checks for
missing, incorrect, inconsistent, unsupported, fabricated, or
policy-violating information
- Validate
AI outputs against defined business rules, data models, guardrails,
expected outcome ranges, and acceptance thresholds
- Determine whether
issues originate in source data, migration logic, data models, integration
layers, AI-agent behavior, guardrails, or front-end
presentation
- Develop
integration and regression tests covering common, negative, edge-case, and
AI-output validation scenarios
- Work
directly with US-based Product, Data Intelligence, Engineering, and
business teams
- Clearly
communicate defects, evidence, quality risks, guardrail gaps, and test
findings to stakeholders
Requirements
Required Experience and Skills
- 1–3
years of QA experience, with a strong focus on API, integration, data
validation, or migration testing
- Proven
experience validating data integrity, completeness, accuracy,
and transformation during migration testing
- Ability
to understand what data is being used, trace it from source to target,
and validate it as it moves through system workflows
- Strong
experience validating complex data models, API payloads, JSON
structures, mappings, and schema transformations across multiple
systems
- Experience
testing AI, LLM, or agentic AI applications, including non-deterministic
and rules-driven outcomes
- Exposure
to agentic testing approaches, including validating AI-agent decisions,
tool usage, fallback behavior, and workflow outcomes
- Experience
defining or validating guardrails, acceptance criteria, and
validation checks for AI-generated outputs
- Strong
API testing experience using tools such as Postman
- Ability
to create detailed test cases focused on data integrity, mapping,
transformation, AI-output validation, and edge-case scenarios
- Strong
functional, integration, regression, analytical, investigative, and
defect-isolation skills
- Strong
verbal and written communication skills, with the ability to work directly
and independently with US-based stakeholders
- Ability
to explain data-integrity issues, AI behavior, guardrail failures,
and non-deterministic outcomes to technical and non-technical
stakeholders
Preferred Experience
- Insurance
domain experience, preferably in commercial insurance, quoting,
underwriting, or insurance application workflows
- Familiarity
with Model Context Protocol (MCP)
- Experience validating structured
data consumed or generated by AI systems
- Understanding
of AI evaluation methods, acceptable outcome ranges, hallucination checks,
validation thresholds, and guardrail effectiveness
- SQL
or similar data-querying skills
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