What the work actually is

The listing is posted under a backend engineering title, but the requirements and responsibilities describe a QA engineering role on the UC-003 Billing Prep program. Read it that way before you apply. Day to day you are writing pytest suites against REST endpoints and rules-engine behaviour, querying Cloud SQL to confirm that charges, invoices and reconciliation records landed in the state the spec claims, and validating the AI-augmented parts of the pipeline — model output accuracy against a labelled baseline, confidence threshold behaviour, and whether low-confidence items correctly escalate into the human review workbench.

The other half is process. You define UAT entry and exit criteria, run sessions with business stakeholders who own billing rather than software, triage what they report into Jira with reproduction steps an engineer can act on, and enforce CI/CD quality gates that block deployment. The program has a stated UAT completion target of late November 2026, so expect release-calendar pressure and the awkward conversations that come with being the last gate.

What the screen looks for

  • Concrete Python automation history — pytest, requests, fixtures, test data management — not familiarity with test tooling in the abstract
  • SQL you can talk through: joins, aggregations, and the queries you actually use to prove a transformation was correct
  • Real billing or financial pipeline exposure, because charge reconciliation defects are domain defects before they are code defects
  • Judgment on AI outputs: how you decide an accuracy threshold is met, how you test escalation paths, how you handle non-deterministic output in a regression suite
  • Evidence you have facilitated UAT with non-technical business owners and held an exit criterion under pressure

Logistics

Full-time engagement, based at the Hyderabad office — this is not a remote or async listing, and location is a hard gate. Target band is 3–5 years of experience. Pay is undisclosed on the platform; expect compensation to be discussed after the technical screen rather than surfaced up front. Screening on Turing is AI-led and follow-up heavy: expect the interviewer to pick one project you mention and push into specifics for several turns.