AI System Quality Assurance Engineer – Data & Agentic AI

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