Test Lead (AI Validation & Integration)
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 23h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 269d ago
The date the source published, not the day we noticed it (2026-02-06). Last seen at its source 1h ago.
We have tracked this listing since 12 Oct 2026 (21 days). The employer's own board has carried it every time we have read it, most recently 1 hour ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Poland
The description states no restriction of its own. This is the source's own tag.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Recruitee employer's own board first seen 21d ago · last seen 1h ago
The listing
We are looking for an experienced Test Lead to drive the Quality and Validation strategy for a high-impact solution, in a highly regulated industry, involving AI components and Salesforce integration. In this role, you will own both definition and delivery of the Testing & AI Validation Strategy. You will be responsible for defining how solution outputs are verified for correctness, reliability, and safe operation, bridging the gap between newly developed AI Agents components and the client’s Salesforce environment.
What you’ll do
Strategy & Planning:
Own the QA Strategy: Design, plan and dDevelop the comprehensive Testing and AI Validation Strategy, covering FunctionalUnit, E2E, Integration, Regression, and User Acceptance Testing (UAT).
Address NFR and align against standards (e.g. GDPR, HIPAA, HITRUST, etc.)
AI Validation Logic: Define validation plans for AI components, specifically determining what constitutes "correct" output, setting success thresholds, and establishing evaluation methods (including handling hallucinations and grounding rules).
Environment & Data: Create detailed plans for Test Environments (Dev/Test/Prod) and define the Test Data Strategy to ensure sufficient coverage for standard scenarios, edge cases, and ingestion failures.
Execution & Reporting:
Hands-on Testing: Execute functional, regression, exploratory, performance, and load tests.
Integration Coordination: Coordinate "Joint End-to-End Validation" efforts, collaborating with the client team to manage testing of Salesforce integration
Pipeline Integration: Integrate testing activities into the CI/CD pipeline for continuous quality assurance
Process Management: Manage Jira test case structures aligned to user stories and acceptance criteria
Metrics: Provide regular reporting, tracking key QA metrics such as defect ratios
About the project, in short. Is it a new project or maintenance?
This is a new project for a client that we have already established collaboration with during other projects. We are finalizing the Discovery Phase for this project and moving into Delivery.
We are developing a data validation and automation engine that processes structured and unstructured inputs against a complex logic framework. The system utilizes AI models to interpret user-submitted data, identifying discrepancies and missing information before mapping the results to a structured output format. Key components include automated ingestion, rule-based validation logic, and integration with existing enterprise architecture in a highly regulated industry.
Technical Requirements (must-have):
Fluent English
QA Strategy: Experience defining end-to-end testing strategies for complex solutions
End-to-End Testing: Experience in designing and executing E2E tests for complex, integrated systems using any modern testing technology
AI/ML/LLM Testing Experience: Proven ability to validate AI models
Test Data Management: Experience strategizing and managing test data across multiple environments (Dev/Test/Prod)
Traceability mindset: linking requirements → test cases → evidence → defects → release decisions.
Experience testing NFRs: performance, reliability, auditability, security basics.
Strong understanding of privacy and sensitive data handling in testing (PII/PHI minimization, masking/anonymization)
Hands-on automation experience (at least one strong stack): e.g., Python/Java/JS + frameworks (pytest/JUnit/etc.)
QA Metrics: Ability to track and report on specific QA metrics, such as defect ratios
Good communication skills (written and verbal)
Jira Proficiency
Full time availability, with overlap during 14:00-18:00 - to ensure seamless collaboration with USA based client
Additional Requirements (nice-to-have):
Familiar with AWS platform
ISTQB or similar certifications
Previous experience in fast-paced consulting or client-facing projects