AI Voice Copilot Architect (Real-Time Voice Pipeline)

Objective

Own the technical architecture and delivery of the voice copilot from validated PoC to production.

• Hit the bar this client tests against: latency, accuracy, concurrency, and cost.
• Keep expectations aligned: production polish is in scope now; protect the team from silent scope creep.
• Transfer knowledge continuously to the client's team and Neurons Lab engineers.

Areas of Responsibility

Technical architecture & hands-on implementation

• Own the full pipeline: streaming speech-to-text, LLM field extraction, Chrome-extension delivery, and AWS infrastructure.
• Drive latency work: cut P95 from ~6s toward ~2s; remove post-processing corner cases (occasional ~1min lag on one field type).
• Run model A/B tests (current pair: Claude Haiku vs GPT Luna) with golden-set evaluation for phonetic name and email accuracy.
• Own evaluation and cost: Langfuse traces, accuracy dashboards, real per-call cost from live calls, and an optimization plan.
• Harden for production: 5–10+ concurrent calls, strict data isolation between users, monitoring, alerting, and safe rollback.
• Ship epics end to end (example: the SES email briefing service); always keep a demo fallback so a live session never fails.

Working with client stakeholders

• Front technical discussions with a meticulous client; VCCs test edge cases and expect production quality.
• Present concrete system behavior, with numbers — this account rewards evidence, not slides.
• Hold the scope line: tie every feedback item to the SOW; route roadmap items (learning loop, persistent memory) to future phases.
• Keep internal discussions internal; all client-facing materials pass ADM review before sending.

Team & knowledge

• Lead the AI Engineer and the pod: set tasks, review output, unblock fast.
• Absorb the handover from the outgoing architect (0.15–0.2 FTE supervision window) and become independent fast.
• Run knowledge-transfer sessions; the project must have no single point of failure.
• Support the production SOW with estimates and architecture options when the account team asks.

Skills

• Real-time voice pipelines: streaming STT, turn handling, low-latency LLM inference — hands-on.
• LLM engineering: prompt engineering, structured extraction, guardrails, model A/B evaluation.
• Observability and evals: Langfuse or similar; golden datasets; latency, accuracy, and cost dashboards.
• AWS: Bedrock, serverless patterns, SES; token economics and per-call cost engineering.
• Full-stack pragmatism: strong Python; enough TypeScript / Chrome-extension knowledge to own the integration.
• Clear spoken and written English for demanding US executives.

Knowledge

• Contact-center / agent-assist patterns and metrics (handle time, cost per call, concurrency).
• Production LLM operations: load testing, data isolation, incident handling.
• Nice to have: empathy-sensitive domains (healthcare, veterinary, insurance) and PE-sponsored rollouts.

Experience

Key characteristics (screen for all four):

1. Voice AI in production — mandatory. Shipped at least one real-time voice or speech product to real users (agent assist, voice bot, live transcription copilot). Candidates will demo real artifacts at the interview.
2. 6+ years hands-on AI/ML engineering, with strong recent LLM production practice.
3. Latency and reliability record. Can show measured P95 reductions and concurrency fixes on a live system.
4. Consulting / client-facing seniority. Calm and precise under detailed UAT scrutiny; manages expectations well.

Nice to have:

• Chrome extension delivery; telephony / streaming stacks (Amazon Connect, Twilio, LiveKit).
• Langfuse in production.
• US client experience with Eastern-time overlap.