QA Lead, AI Agents

Rifa AI is building the AI agents platform for contact centers in regulated industries.

Enterprises in these industries want AI agents handling their customer operations and mostly can't deploy them. It's not a model problem. Horizontal platforms lack governance, release processes, and change management, and in a domain where every call can be reviewed by a regulator, that's disqualifying. Building an AI agent has never been easier. Deploying one an enterprise can trust has never been harder. That harder problem is the one we work on.

Our platform turns a company's written procedures into AI agents: voicebots that hold real-time conversations, take actions in the client's CRM, and stay within the limits the client has set. Every release is gated by an automated testing suite, and every conversation feeds a post-analysis platform. We're live in production today, handling debt collection calls for US financial services clients.

The engineering convictions behind it: evaluation methodology, not model capability, is the bottleneck. Every rule and decision trace becomes part of a company's context graph. Observability goes beyond logging. And our Agent Studio lets engineers, non-engineers, and auditors collaborate to build and improve AI agents with the right guardrails to achieve the expected business outcomes.

Rifa was founded by Sameer Fulzele (IIT Bombay). We're a small team of exceptionally capable, passionate engineers with paying enterprise clients and growing revenue, backed by Seaborne Capital, a founder-first firm of exceptional industry operators that works closely with its founders, and by angel investors who are veterans of enterprise software and operators in the accounts receivable industry.

This is the hands-on quality and operations role behind our enterprise client relationships. You'll live in the transcripts and performance data: auditing how our 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 also lead our QA team, and work side by side with our Agent Engineers the way a forward-deployed pair does: they own a client's agent, you own its quality.

You'll be client-facing: on audit calls, walking client-side QA and compliance teams through findings, and reconciling our 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.

• Lead the QA team. Set the audit standards and scorecards, review the team's work, coach analysts, and decide what gets escalated and when.

• Pair with Agent Engineers. Work alongside the Agent Engineer who owns each client's agent, the way a forward-deployed team does: you bring the evidence from transcripts and audits, they ship the fix, and you check it held in production.

• 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.

• At least 2 years in QA, operations, quality analysis, delivery support, or a similar client-facing role, ideally at a B2B SaaS, financial-services or consulting company.

• Experience leading or mentoring a QA or operations team: setting the standard, building the process, reviewing others' work, and holding the bar.

• 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.

• 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

• Experience building or running testing and auditing frameworks: test plans and regression suites, QA scorecards, or compliance audit programs

• An interest in AI / conversational systems and how they behave in production

Small teams per client, with real ownership. We review each other's work, we write things down, and we'd rather hear an honest "I don't know yet" than a confident wrong answer.

• Trust: We do the right thing, especially when nobody is checking. Clients hand us regulated conversations with their own customers, and we earn that every day.

• Transparency: We write things down, share the real numbers, and say "I don't know yet" out loud, with each other and with clients.

• Technically best solution: We choose what's right, not what's easiest or trendiest. When we notice we got it wrong, we fix it.

• Decisiveness: We decide quickly with the information we have, commit, and correct course fast when reality disagrees.

• Simplicity: We keep systems, processes, and words simple. Complexity is a cost we pay only when it clearly buys something.