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.
Rifa ships voice and chat agent releases to multiple enterprise clients on overlapping timelines, and every one of those releases has an eval gate, a client sign-off, and a rollout plan. You'll own that machinery, reporting directly to the CEO. As Technical Program Manager you run the release train end to end: planning what lands in each cycle, tracking dependencies across engineering, QA, and client-facing teams, catching risk before it becomes a slipped date, and making sure a release that passes internally actually lands cleanly with the client.
This is a role for someone who reads engineering tickets and QA reports as fluently as a project plan, and who keeps five threads moving without letting any of them become someone else's surprise.
Run the release train. Own the release calendar across voicebot and chatbot versions and across clients. Plan each cycle's scope with engineering, hold the eval gate as a hard line, and manage staged rollouts so no client is surprised by what changes.
Track dependencies and kill risk early. Map what blocks what across engineering, QA, infrastructure, and client schedules. Surface slippage when it's a one-day problem, not a one-week one, and drive the trade-off conversation to a decision.
Own program communication. Keep a single, current source of truth for every program: status, risks, decisions, and owners, written clearly enough that the CEO, an engineer, and a client-facing teammate all act from the same picture.
Run client onboarding and migration programs. When a new client goes live or an existing one moves to a new agent version, you own the plan: milestones, environment readiness, data and integration checkpoints, sign-offs, and the go-live itself.
Improve the machinery. Where the process creates friction, fix the process. Where a checklist would have caught the incident, write the checklist. Keep it simple; process is a cost we pay only when it clearly buys something.
4 to 7 years in technical program management, release management, or engineering-adjacent project management at a B2B software company, with at least part of that in a production environment where releases had real consequences.
Enough technical depth to be dangerous: you can read an engineering ticket, follow an architecture discussion, and understand why a failing eval blocks a release, without needing an engineer to translate.
A track record of shipping cross-team programs on time, and of being honest early when they won't be.
US-hours overlap: reliable daily overlap with US business hours (ET/CT), which in practice means a late-afternoon-into-evening IST schedule, since client rollouts and go-lives happen on their clock.
Strong written and spoken English. Status updates, rollout plans, and risk callouts are the core artifacts of this job, and they need to hold up under scrutiny.
Experience shipping AI/ML or conversational AI products, or working with eval-gated release processes
Experience in a regulated domain: financial services, collections, healthcare, insurance
Familiarity with observability and QA tooling, or with client-facing rollout and migration programs
Early-stage startup experience where you built the process rather than inherited it
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.
Compensation Range: ₹25L - ₹50L
