AI Architect (Voice AI)

Next is the production phase.

Stage: production SOW in executive alignment; start expected September 2026.

Duration: multi-month, with strong extension probability. 0.5 FTE minimum; ramp toward 1.0 FTE as production scales.

Why the role is open: the current architect moves to another strategic build. He stays at 0.15–0.2 FTE for supervision and knowledge transfer during ramp-up, so the new architect gets a structured handover.

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