Senior Platform Engineer

Job Description

About us 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. About the role While Agent Engineers own individual client deliveries, you own the platform underneath all of them: the runtime that agents execute on, the voice infrastructure that keeps live calls fast and stable, the enterprise capabilities that get us through security reviews, and the intelligence layer that turns millions of conversations into insight. This is the highest-leverage engineering seat in the company: every improvement you ship reaches every client at once. You'll work directly with the founder on architecture and own your decisions end to end, from design through production and the incident channel. What you'll do Build the agent platform primitives. Design and evolve the runtime, orchestration engine, and the systems behind Agent Studio: how agents are defined from written procedures, how they reason and take actions, and how guardrails make out-of-bounds behavior impossible rather than unlikely. Building agents is its own engineering discipline, and you'll be building the tooling that discipline needs. Own the voice infrastructure. Real-time audio streaming over WebSockets, STT and TTS integration at low latency, and the hard parts of live conversation: interruptions, disconnects, transfers, and telephony integrations that pick up and place calls reliably at growing volume. Form the foundation of enterprise trust. Clients hand us regulated conversations with their own customers. You'll build and uphold the capabilities that make that possible: RBAC, access management, audit logs, data isolation, and integrations that embed into complex client environments while surviving their security reviews. Build the evaluation layer. We believe evaluation methodology, not model capability, is the bottleneck, and you'll own the system that proves it: the DeepEval-driven suite that replays real conversations against every change, simulation of the scenarios we haven't seen yet, and experimentation frameworks that let us A/B test agent behavior and make changes with evidence instead of intuition. Every release gates on what you build here. Build the observability layer. Observability goes beyond logging. You'll extend Reflect so that every agent decision is traceable: what the system knew at each moment, which instructions the model was given, and why it responded the way it did. Add proactive monitoring that surfaces regressions, drift, and new conversation patterns before a client notices, and turn millions of calls into insight the whole company acts on. Close the feedback loop. Connect what evaluation and observability find back into how agents improve: recurring failure modes become new eval cases, production patterns reshape procedures, and agents get measurably better over time. Keep the platform fast, reliable, and boring to operate. Own Kubernetes, ArgoCD-driven GitOps deployments, CI/CD, and observability with SigNoz. Build the self-serve infrastructure that lets the rest of engineering ship without waiting on you, and lead incident response and postmortems when things break. What we work with Python with FastAPI, PostgreSQL, Temporal for background workflows, WebSockets holding real-time conversations open, and STT and TTS providers on the speech side, with LLMs via OpenAI and similar