Senior Platform Engineer

What matters most

• Real-time Voice Infrastructure Reliability

  The role requires managing low-latency audio streaming, STT/TTS integration, and telephony reliability. You must ensure that live conversations remain stable and performant, as these are critical for contact center operations where interruptions or disconnects directly impact service quality.

• Evaluation and Observability Frameworks

  You are responsible for building the evaluation layer using DeepEval and extending observability beyond standard logging. This is a priority because the company views evaluation methodology as the primary bottleneck for deploying AI agents in regulated industries, requiring rigorous testing and decision traceability.

• Enterprise Security and Governance

  You must implement and maintain enterprise-grade security features including RBAC, audit logs, and data isolation. This is essential for meeting the strict compliance requirements of clients in regulated industries who must review every interaction.

• Platform Primitives and Developer Tooling

  You will own the runtime and orchestration engine that powers all client agents. A key priority is building self-serve infrastructure and Agent Studio tools that allow other engineers to ship features efficiently without relying on manual platform intervention.

Likely challenges to think through

• Balancing Latency with Complex Logic

  Maintaining sub-second latency for real-time voice while executing complex orchestration and LLM-based reasoning is a significant technical hurdle. Prepare by demonstrating how you optimize distributed systems for streaming data and handle backpressure in high-concurrency environments.

• Rigorous Compliance in AI Deployments

  The challenge lies in ensuring AI agents remain within strict, regulator-approved boundaries while maintaining conversational flexibility. Prepare by discussing how you design systems that make 'out-of-bounds' behavior technically impossible through architectural guardrails rather than just prompt engineering.

• High-Leverage Decision Making

  As the owner of the core platform, your architectural choices impact every client delivery simultaneously. This creates high pressure for stability. Prepare by highlighting your experience with incident response, postmortems, and how you approach 'boring' but reliable technology choices to minimize production risk.

Topics to prepare

• Real-time Systems and Latency Management

  The role requires managing voice infrastructure with strict latency budgets for STT/TTS and audio streaming. Review concepts of backpressure, jitter, and the differences between request-response APIs and persistent WebSocket connections. Exercise: Design a high-level architecture diagram for a low-latency voice bot, identifying potential bottlenecks in the audio processing pipeline and proposing mitigation strategies for network-induced latency.

• Enterprise Security and Compliance Architecture

  Rifa AI operates in regulated industries requiring robust trust primitives. Focus on implementing RBAC, audit logging, and data isolation. Exercise: Outline a strategy for implementing fine-grained access control in a multi-tenant environment using OpenFGA, specifically addressing how to ensure data isolation between different enterprise clients.

• Evaluation-Driven Development for AI Agents

  The platform prioritizes evaluation methodology over raw model capability. Study how to build automated testing suites that replay conversations to detect regressions. Exercise: Create a test plan using DeepEval to simulate a specific failure mode in a customer support agent, such as an agent failing to adhere to a specific compliance guardrail, and define the metrics for success.

Skills to revise

• Kubernetes and GitOps Deployment

  The platform relies on Kubernetes and ArgoCD for reliable, automated deployments. Exercise: Create a sample ArgoCD application manifest that includes a health check and a sync policy, demonstrating how to automate a deployment pipeline for a Python-based microservice.

• Observability and Incident Response

  The role involves building observability systems that extend beyond simple logging to provide decision traceability. Exercise: Configure a mock OpenTelemetry trace for an agent decision-making process, ensuring that the trace captures the model's input context, the specific instructions provided, and the final output to allow for post-incident root cause analysis.

• Distributed Systems with Temporal

  Temporal is used for managing complex background workflows. Exercise: Model a multi-step agent interaction (e.g., call initiation, CRM lookup, and response generation) as a Temporal workflow, focusing on how to handle retries and state persistence during transient failures.

• Prepare a distributed systems architecture walkthrough

  Draft a technical summary of a production system you have managed, focusing on how you handled Kubernetes orchestration, GitOps workflows with ArgoCD, and incident response. Be prepared to explain your decision-making process for choosing specific tools and how you ensured system reliability under load.

• Review real-time audio and latency management

  Reflect on your experience with WebSockets, SIP, or WebRTC. Prepare to discuss how you have managed latency budgets and backpressure in streaming systems, as this is critical for the voice infrastructure and STT/TTS integration required by the role.

• Develop an evaluation and observability strategy

  Prepare to discuss how you would build an evaluation layer for AI agents. Explain how you would use tools like DeepEval or OpenTelemetry to ensure decision traceability, surface regressions, and connect production feedback loops to system improvements.

• Formulate questions on enterprise security and compliance

  Prepare thoughtful questions regarding how the platform handles RBAC, data isolation, and audit logs in regulated industries. Demonstrate your understanding of the challenges in deploying AI agents that must adhere to strict enterprise governance and security reviews.