• Improving architecture across backend services and platform infrastructure, and defining long-term architectural strategy and technical standards across teams • Establish engineering standards that improve consistency, maintainability, reliability, and operational readiness • Scale distributed systems and improve performance across core services • Improve production engineering practices, including observability, testing, incident response, and post-incident review • Identify and reduce high-impact technical debt • Improve developer tooling, workflows, and engineering productivity • Help teams use agentic development tools effectively across planning, implementation, testing, review, and validation • Partnering with product and infrastructure teams on scalable, resilient systems and subsequently simplifying systems and reducing organizational complexity as the platform scales • Mentor Senior and Staff engineers through architecture reviews, technical coaching, and project guidance • Serve as a trusted technical advisor for difficult engineering and architectural decisions

• Design and implement multi-file coding tasks across bug fixing, feature development, refactoring, and testing • Write clear natural-language specifications and reference implementations • Develop and extend unit and integration test suites • Review peer-generated tasks for correctness, clarity, and realism • Identify edge cases, ambiguities, and potential failure modes • Ensure alignment between specifications, code, and expected outputs

• Own the end-to-end voice AI architecture — from Twilio media streams through LLM orchestration to TTS output and call disposition. • Design and implement multi-agent systems using tool calling, agent handoffs, and shared conversation state for complex patient workflows. • Build and optimize real-time audio pipelines — WebSocket streaming, codec handling (mulaw/PCM), VAD configuration, and interruption management. • Architect analytics and observability infrastructure for voice-specific metrics: per-segment latency (STT/LLM/TTS), call efficacy, disposition accuracy, and ASR error rates. • Solve voice-specific challenges: turn-taking timing, silence detection thresholds, barge-in recovery, medical term recognition, and end-to-end latency optimization. • Integrate voice agents with internal services via secure authenticated APIs. • Drive platform reliability — eliminate single points of failure, implement multi-provider LLM failover, and design graceful degradation paths. • Collaborate with product and clinical operations to improve self-serve efficacy rates and reduce call escalations. • Mentor team members on voice AI best practices and contribute to architectural decisions.

• Take ownership of assigned voice AI features and components, guiding them through the full software development lifecycle. • Contribute to design discussions, code reviews, and best practice adoption within the Voice AI team. • Drive technical decisions on voice pipeline optimization — VAD tuning, turn-taking, interruption handling, and latency management. • Plan and prioritize tasks in an Agile environment, ensuring timely and high-quality delivery. • Work with Product Managers and stakeholders to refine requirements and scope technical efforts for conversational AI features. • Monitor voice platform health metrics (call efficacy, ASR accuracy, per-segment latency) and prioritize improvements based on data. • Mentor junior team members, sharing knowledge and best practices in voice AI architecture, TypeScript, and real-time systems. • Encourage a culture of continuous learning and technical excellence through pair programming and design reviews. • Document voice AI patterns, integration contracts, and operational runbooks for the team. • Partner with Product Managers, QA, and clinical operations to gather requirements, validate conversational designs, and guide projects from inception to deployment. • Coordinate with other engineering teams to integrate voice agents with internal services via authenticated APIs. • Work with the analytics team to ensure voice metrics flow correctly through the data pipeline for reporting and optimization. • Drive improvements in our multi-agent orchestration approach — tool calling patterns, agent handoff logic, and state management across conversation turns. • Advocate for high-quality standards and automated testing strategies for conversational AI systems, including voice-specific test patterns (simulated calls, transcript validation, latency benchmarks). • Identify and resolve voice-specific UX issues: ASR errors on medical terminology, silence detection tuning, barge-in recovery, and end-to-end response latency.