Rifa AI announced job vacancy for the post of Client QA & Operations Analyst. The place of posting will be at Remote (Work from Home).Candidates who have completed Bachelor’s Degree is required with 1 to 3 years in QA, operations, quality analysis, delivery support, or a similar client-facing role, ideally at a B2B SaaS, BPO, or financial-services company are eligible to apply. More details about qualifications, job description and roles & responsibilities are as follows
Company Overview
| Name of the Company | Rifa AI |
| Required Qualifications | Graduate |
| Skills | QA tooling, call-quality scoring, or observability/audit dashboards |
| Category | Product |
| Work Type | Remote |
This is the hands-on quality and operations role behind their enterprise client relationships. You’ll live in the transcripts and performance data: auditing how their AI agents handle real client conversations, catching issues before and after clients do, and turning what you find into clear, actionable work for the engineering team. You’ll be client-facing: on audit calls, walking client-side QA and compliance teams through findings, and reconciling their read of bot performance with theirs. It’s a role for someone precise, curious, and organized, comfortable with data and detail, and confident explaining what they found and why it matters.
You’ll report to the Technical Program / Product Manager, who owns the overall client relationship, and you’ll be their eyes and ears on day-to-day quality.
Job Details
Θ Positions: Client QA & Operations Analyst
Θ Job Location: Remote (Work from Home) – Hiring office Bangalore
Θ Salary: INR 8 – 15 LPA
Θ Job Type: Full Time
Θ Requisition ID: NA
Roles and Responsibilities:
- Audit transcripts and bot performance. Review call and chat transcripts in Reflect, our observability platform, evaluate how our AI agents performed against quality and compliance standards, and score conversations consistently and fairly.
- Find and document issues. Spot regressions, compliance gaps, mishandled flows, and edge cases in agent behavior, and document them clearly, with the transcript or metric as evidence.
- Convey issues to engineering. Translate what you find into well-written Linear cards that engineers can act on without a follow-up call: clear repro steps, the affected conversation, and why it matters to the client.
- Get on client calls. Join client QA and audit calls, walk their teams through findings, and answer questions on how our agents are performing.
- Reconcile audits with the client. Compare our QA findings against the client’s own audit team’s results, work through discrepancies, and align on a shared, defensible view of quality.
- Support the client relationship. Feed insights, recurring issues, and trends back to the Technical Program / Product Manager to inform release communications, weekly syncs, and monthly reviews.
Required Skills & Qualifications:
- 1 to 3 years in QA, operations, quality analysis, delivery support, or a similar client-facing role, ideally at a B2B SaaS, BPO, or financial-services company.
- Comfort with data and transcripts as evidence. You can read a call log or a QA scorecard, form your own view, and back it up, not just relay what someone else said.
- Clear written and spoken English. A large share of this job is written: issue tickets, audit notes, and client-facing summaries that hold up under scrutiny.
- US-hours overlap. Reliable daily overlap with US business hours (ET/CT), which in practice means a late-afternoon-into-evening IST schedule, and comfort being on a call with a client’s ops or compliance team.
- Precision and follow-through. You’re organized, detail-oriented, and you close the loop on what you find.
Even better
- Experience in debt collections, lending, or another regulated financial-services domain, or familiarity with FDCPA / compliance workflows
- Hands-on experience with QA tooling, call-quality scoring, or observability/audit dashboards
- Familiarity with Linear (or similar issue trackers like Jira) and how good engineering tickets are written
- An interest in AI / conversational systems and how they behave in production
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